U.S. patent application number 09/808688 was filed with the patent office on 2001-09-06 for fully-coupled positioning system.
Invention is credited to Lin, Ching-Fang.
Application Number | 20010020216 09/808688 |
Document ID | / |
Family ID | 22731435 |
Filed Date | 2001-09-06 |
United States Patent
Application |
20010020216 |
Kind Code |
A1 |
Lin, Ching-Fang |
September 6, 2001 |
Fully-coupled positioning system
Abstract
A positioning system is disclosed for measuring a position of a
vehicle on land, air, and space, using measurements from a global
positioning system receiver and an inertial measurement unit. In
the present invention, an integrated Kalman filter processes the
all-available measurements of the global positioning system:
pseudorange, delta range, carrier phase, and the solution of an
inertial navigation system. The integrated Kalman filter is a
multi-mode, robust kalman filter, in which optimal integrated mode
is selected based on the measurement availability and filter
stability. The high accurate solution of the inertial navigation
system, which is corrected by the Kalman filter, is used to aid
on-the-fly resolution of the carrier phase integer ambiguity of
global positioning system in order to incorporate the carrier phase
measurements into the Kalman filter, and to aid the carrier phase
and code tracking loops of the receiver of the global positioning
system to improve the receiver jamming and high dynamic
resistance.
Inventors: |
Lin, Ching-Fang;
(Chatsworth, CA) |
Correspondence
Address: |
Raymond Y. Chan
1050 Oakdale Lane
Arcadia
CA
91006
US
|
Family ID: |
22731435 |
Appl. No.: |
09/808688 |
Filed: |
March 14, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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09808688 |
Mar 14, 2001 |
|
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09197958 |
Nov 20, 1998 |
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Current U.S.
Class: |
701/472 |
Current CPC
Class: |
G01S 19/44 20130101;
G01C 21/165 20130101; G01S 19/49 20130101; G01S 19/26 20130101 |
Class at
Publication: |
701/216 |
International
Class: |
G01C 021/26 |
Goverment Interests
[0002] This invention was made with Government support under
Contract No. F0863097-C-0045 awarded by the Air Force Armament
Directorate of Wright Laboratory (WL/MNAG), Eglin Air Force Base,
FL 32542-6910. The Government has certain rights in the invention.
Claims
What is claimed is:
1. A fully-coupled positioning system, comprising: a global
positioning system (GPS) radio frequency (RF) unit for receiving
global positioning system (GPS) radio frequency (RF) signals,
amplifying said GPS RF signals and down converting said GPS RF
signals into intermediate frequency (IF) signals; a GPS digital
signal processing unit for sampling said IF signal and deriving GPS
pseudorange, delta range, and carrier phase measurements; an IMU
data sampling unit for collecting angular rate and acceleration
measurements of said vehicle from an inertial measurement unit
(IMU); a centralized navigation Kalman filter for receiving and
processing said GPS pseudorange, delta range, and carrier phase
measurements for said GPS and said angular rate and acceleration
measurements from said IMU; wherein said centralized navigation
Kalman filter computes an initial inertial navigation solution
based on initial position, velocity, and attitude measurements;
wherein said centralized navigation Kalman filter resolves a
carrier phase ambiguity to obtain carrier phase ambiguity numbers
and detects cycle slip to obtain a plurality of slip cycles based
on said GPS pseudorange, said delta ranges, said carrier phase
measurements, and said inertial measurements; wherein said
centralized Kalman filter computes a fully-coupled positioning
solution for each epoch; at least an interface for realizing said
data and control communication with other avionics systems; and a
time synchronizer for providing said time signal and local
reference signal to said avionics system devices.
2. A fully-coupled positioning system, as recited in claim 1,
further comprises of at least one external sensor carried by said
vehicle, wherein said external sensor provides a plurality of
initial attitude measurements for said vehicles.
3. A fully-coupled positioning system, as recited in claim 1,
wherein said initial attitude measurements are provided from a GPS
receiving set.
4. A fully-coupled positioning system, as recited in claim 1,
wherein said inertial measurement unit is a tactical inertial
measurement unit installed on a platform and said external sensor
is a platform inertial measurement unit, wherein measurements or
navigation parameters of said tactical inertial measurement unit
and said platform inertial measurement unit are filtered by an
alignment filter to provide optimal initialization information for
said tactical inertial measurement unit.
5. A fully-coupled positioning system, as recited in claim 1,
wherein said inertial measurement unit is a tactical inertial
measurement unit installed on a platform, wherein measurements or
navigation parameters of said tactical inertial measurement unit
are filtered by an alignment filter to provide optimal
initialization information for said tactical inertial measurement
unit.
6. A fully-coupled positioning system, as recited in claim 1, said
GPS frequency signals are tracked by a plurality of carrier phase
locked loops and a plurality of code tracking loops of said GPS
receiving set, and said carrier phase locked loops and said code
tracking loops of said GPS receiving set are aided by said
fully-coupled positioning solution.
7. A fully-coupled positioning system, as recited in claim 1,
wherein a plurality of errors of said inertial navigation solution
are removed with said optimal estimates of said inertial navigation
solution errors.
8. A fully-coupled positioning system, as recited in claim 1,
wherein said centralized Kalman filter is a multi-mode robust
Kalman filter that selects an operation modes from the following
operation modes: a GPS/INS position and velocity integration mode,
wherein said position vector and said velocity vector from said GPS
receiving set are combined with said inertial navigation solution
to derive an integrated navigation solution; a GPS/IMU pseudorange
and delta range integration mode, wherein said GPS pseudoranges and
said delta ranges are combined with said inertial navigation
solution to derive an integrated navigation solution; a GPS/IMU
pseudorange and delta range integration mode with aiding of GPS
tracking loops, wherein said GPS pseudoranges and said delta ranges
are combined with said inertial navigation solution to derive an
integrated navigation solution, wherein said global positioning
system radio frequency signals are tracked by a plurality of
carrier phase locked loops and a plurality of code tracking loops
of said GPS receiving set, and said carrier phase locked loops and
said code tracking loops of said GPS receiving set are aided by
said fully-coupled positioning solution obtained in step (g); a
GPS/IMU pseudorange, delta range, and carrier phase integration
mode, wherein said GPS pseudoranges, said delta ranges, and said
carrier phase measurements are combined with said inertial
navigation solution to derive an integrated navigation solution;
and a GPS/IMU pseudorange, delta range, and carrier phase
integration mode with aiding of GPS tracking loops, wherein said
GPS pseudoranges, said delta ranges, and said carrier phase
measurements are combined with said inertial navigation solution to
derive an integrated navigation solution, wherein said global
positioning system radio frequency signals are tracked by a
plurality of carrier phase locked loops and a plurality of code
tracking loops of said GPS receiving set, and said carrier phase
locked loops and said code tracking loops of said GPS receiving set
are aided by said fully-coupled positioning solution.
9. A fully-coupled positioning system, as recited in claim 1,
wherein said angular rate and said acceleration measurements from
said inertial measurement unit is processed to derive said inertial
navigation solution, and is passed to said integrated Kalman
filter, wherein a plurality of error estimates, which are provided
by said integrated Kalman filter, are fed back to said inertial
navigation solution to remove errors of said position vector, said
velocity vector, and inertial sensors.
10. A fully-coupled positioning system, as recited in claim 1,
wherein said GPS radio frequency signals are received by a GPS
antenna and processed in a GPS RF/IF Unit, carrier and code
tracking loops, an ambiguity resolution and a message decoding
being passed to said integrated Kalman filter, wherein optimal
velocity and acceleration information of said integrated Kalman
filter are fed back to said carrier and code tracking loops to aid
a GPS signal tracking process, moreover position, velocity,
attitude from said inertial navigation solution being input to said
ambiguity resolution to aid a GPS carrier phase integer ambiguity
resolution.
11. A fully-coupled positioning system, as recited in claim 1,
wherein said centralized Kalman filter determines said carrier
phase ambiguity numbers; when said carrier phase ambiguity numbers
are not available, said centralized Kalman filter selects one of
said GPS/IMU position and velocity integration mode, said GPS/IMU
pseudorange and delta range integration mode, and said GPS/IMU
pseudorange and delta range integration mode with aiding of GPS
tracking loops; when said carrier phase ambiguity numbers are
available, said Kalman filter switches to one of said GPS/IMU
pseudorange, delta range, and carrier phase integration mode and
said GPS/IMU pseudorange, delta range, and carrier phase
integration mode with aiding of GPS tracking loops.
12. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/INS P-V
(position and velocity) integration mode and derives an accurate
integrated navigation solution which is used to reduce measurement
errors in said GPS pseudorange, said delta ranges, and said carrier
phase measurements so as to facilitate searching for said carrier
phase ambiguity numbers; wherein after obtaining said carrier phase
ambiguity numbers said centralized navigation Kalman filter
switches to said GPS/IMU pseudorange, delta range, and carrier
phase integration mode.
13. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/IMU
pseudorange and delta range integration mode and derives an
accurate integrated navigation solution which is used to reduce
measurement errors in said GPS pseudorange, said delta ranges, and
said carrier phase measurements so as to facilitate searching for
said carrier phase ambiguity numbers; wherein after obtaining said
carrier phase ambiguity numbers said centralized navigation Kalman
filter switches to said GPS/IMU pseudorange, delta range, and
carrier phase integration mode.
14. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/IMU
pseudorange and delta range integration mode with aiding of GPS
tracking loops and derives an accurate integrated navigation
solution which is used to reduce measurement errors in said GPS
pseudorange, said delta ranges, and said carrier phase measurements
so as to facilitate searching for said carrier phase ambiguity
numbers; wherein after obtaining said carrier phase ambiguity
numbers said centralized navigation Kalman filter switches to said
GPS/IMU pseudorange, delta range, and carrier phase integration
mode.
15. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/INS position
and velocity integration mode and derives an accurate integrated
navigation solution which is used to reduce measurement errors in
said GPS pseudorange, said delta ranges, and said carrier phase
measurements so as to facilitate searching for said carrier phase
ambiguity numbers; wherein after obtaining said carrier phase
ambiguity numbers said centralized navigation Kalman filter
switches to said GPS/IMU pseudorange, delta range, and carrier
phase integration mode with aiding of GPS tracking loops.
16. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/IMU
pseudorange and delta range integration mode and derives an
accurate integrated navigation solution which is used to reduce
measurement errors in said GPS pseudorange, said delta ranges, and
said carrier phase measurements so as to facilitate searching for
said carrier phase ambiguity numbers; wherein after obtaining said
carrier phase ambiguity numbers said centralized navigation Kalman
filter switches to said GPS/IMU pseudorange, delta range, and
carrier phase integration mode with aiding of GPS tracking
loops.
17. A fully-coupled positioning system, as recited in claim 1,
wherein before obtaining said carrier phase ambiguity numbers said
centralized navigation Kalman filter works in said GPS/IMU
pseudorange and delta range integration mode with aiding of GPS
tracking loops and derives an accurate integrated navigation
solution which is used to reduce measurement errors in said GPS
pseudorange, said delta ranges, and said carrier phase measurements
so as to facilitate searching for said carrier phase ambiguity
numbers; wherein after obtaining said carrier phase ambiguity
numbers said centralized navigation Kalman filter switches to said
GPS/IMU pseudorange, delta range, and carrier phase integration
mode with aiding of GPS tracking loops.
Description
CROSS REFERENCE OF RELATED APPLICATION
[0001] This is a divisional application of a non-provisional
application, application Ser. No. 09/197,958, filed Nov. 20, 1998,
now allowed.
TECHNICAL FIELD
FIELD OF THE PRESENT INVENTION
[0003] The present invention relates to a global positioning
system/inertial measurement unit process and system thereof, and
more particularly to a fully-coupled kinematic global positioning
system/inertial measurement unit process and system thereof to
improve the navigation accuracy of a GPS guided vehicle on land,
air, and space.
BACKGROUND OF THE PRESENT INVENTION
[0004] To meet the future applications, it is needed to develop a
reliable, accurate, miniaturized, low cost, kinematic global
positioning system/inertial measurement unit integrated navigation
system which is capable of operating in a high dynamic flight
environment against a mixture of multi-type global positioning
system (GPS) signal loss or deterioration, and improving navigation
accuracy of GPS guided vehicle.
[0005] A major way of reducing cost of a navigation system is to
use cheaper sensors and components that make the integrated
navigation, and guidance and control system designs all the more
challenging. Traditionally, guidance and navigation systems used
for guided vehicle are mainly inertial navigation systems (INS)
which is composed of an inertial measurement unit (IMU) and a
processor. An important advantage of INS guidance is independence
from external support. Unlike other types of guidance, INS devices
can not be jammed or fooled by deceptive countermeasures.
Unfortunately, INS guidance cannot provide high accuracy at long
ranges. Inertial sensors are subject to errors that tend to
accumulate over time--the longer the flight time, the greater the
inaccuracy. The cost of developing and manufacturing a gyroscope
increases as its level of accuracy improves. High-quality
gyroscopes are difficult to manufacture, and only a relatively
small number of companies around the world are capable of producing
them. In part, this reflects the limited market for gyroscopes
suitable for use in a highly accurate INS. Therefore, the inherent
inaccuracy of the INS means that it cannot be the sole guidance
system for a highly accurate tactical missile. Additional inputs
are needed to correct for INS errors.
[0006] More recent developments in satellite navigation techniques
are making possible the precise navigation at low cost. Efforts are
now under way to develop integrated GPS/IMU navigation and guidance
systems, suitable for use in high-jamming and high-dynamic flight
environments. One implication of integrated GPS/IMU packages is
that lower-cost, more easily manufactured IMU sensors can be used.
This can result in significant savings.
[0007] Therefore, the technology trends for inertial sensors, GPS
accuracies, and integrated GPS/IMU systems, including
considerations of jamming and high dynamic, will lead to the one
meter accuracy. The technical challenges come from the improvement
of inertial and GPS sensor performance and the optimal integration
of these sensors in the software and hardware designs. For inertial
sensors, trend-setting sensor technologies are fiber-optic gyros,
silicon micromechanical gyros, resonating beam accelerometers, and
silicon micromechanical accelerometers. The utilization of these
techniques is resulting in low-cost, high reliability, small size,
and light weight for inertial sensors and for the systems into
which they are integrated.
[0008] For the GPS accuracies, the current 16-meter (SEP, spherical
error probable) specified accuracy, or 8 to 10-meter (CEP, circular
error probable) observed accuracy of the GPS PPS (Precise
Positioning Service) provides impressive navigation performance
especially when multiple GPS measurements are combined into a
robust centralized Kalman filter to update an INS. The filter
provides an opportunity dynamically to calibrate the GPS errors, as
well as, the inertial errors, and when properly implemented, CEPs
far better than 8 meters can be obtained. For example, for
precision guidance and automated aircraft landings, the requirement
for accuracy of the integrated navigation systems is less than 3
meters or even better.
[0009] The trend towards improvement of accuracies of the
integrated navigation systems is to utilize kinematic GPS and
develop advanced fully-coupled kinematic GPS/IMU integrated systems
in which both GPS receiver code and carrier tracking loops are
aided with the inertial sensor information. Therefore, the
measurement accuracy and anti-jamming capability of the GPS
receiver can be dramatically enhanced and increased. Rapid carrier
integer cycle ambiguity search and resolution, cycle slip detection
and isolation procedures can also be completed within a few seconds
through use of the inertial aiding information. In addition, GPS
specified and observed current accuracies can be improved due to
various stages of the wide area GPS enhancements.
[0010] As a result, the design and development of kinematic GPS/IMU
integrated navigation systems is extremely challenging.
Specifically, the hardware sensors and software algorithms
constituting the system should satisfy the following
requirements:
[0011] 1. Inertial Sensors
[0012] Major changes are currently underway in technologies
associated with inertial sensors used for stabilization, control,
and navigation. These changes are enabling the proliferation of
inertial sensors into a wide variety of new military and commercial
applications. Main Challenges for design and fabrication of
inertial sensors are low cost, high reliability, accuracy required
by mission, small size, and lightweight.
[0013] (1) Fiber-Optic Gyros (FOG)
[0014] An economical replacement for the ring laser gyro (RLG)
providing the same level of gyro bias performance.
[0015] (2) Silicon Micromechnical Gyros
[0016] Continuous reduction in the gyro drift rate for more
demanding applications.
[0017] (3) Resonating Beam Accelerometers
[0018] (4) Silicon Micromechanical Accelerometers
[0019] 2. GPS receiver
[0020] As regards GPS sensor size, the current GPS receiving card
(OEM, original equipment manufacture) is less than the size of a
cigarette box. Technical trades for design of GPS receivers for GPS
guided vehicle will focus on enhancement of high anti-jamming and
high dynamic performance, and decrease of GPS measurement noise,
including multipath effects.
[0021] (1) Trade-off between tracking loop bandwidth and high
anti-interference of GPS receiver.
[0022] (2) Short time-to-first-fix (TTFF) and signal reacquisition
time.
[0023] (3) Direct rapid P-code tracking and capturing.
[0024] (4) Inertial aiding code and carrier tracking loops.
[0025] (5) Receiver hardware/software digital signal
processing.
[0026] (6) Anti-multipath antenna design.
[0027] 3. Integrated System Algorithms
[0028] In future GPS/IMU integrated navigation systems, the
fully-coupled integration requires that the GPS measurements and
inertial sensor information are directly fused into a centralized
navigation Kalman filter, and outputs of the filter can also aid
the receiver tracking loops to improve the anti-jamming capability
of the GPS receiver. Therefore, the technical challenges will be
the following:
[0029] (1) System reconfiguration based on multiple sensors.
[0030] (2) Multi-mode robust navigation Kalman filter.
[0031] (3) Sensor failure detection and isolation.
[0032] (4) Inertial aiding on-the-fly phase ambiguity resolution
and cycle slip detection.
[0033] (5) Rapid transfer alignment.
[0034] The current technical innovation will contribute
significantly to the prospects for high dynamic vehicle
proliferation. Historically, the most significant obstacles to the
design and development of high dynamic guided vehicle have been the
cost and complexity of vehicle guidance systems.
SUMMARY OF THE PRESENT INVENTION
[0035] The main objective of the present invention is to provide a
fully-coupled positioning process and system thereof, which is an
innovative fully-coupled GPS/INS algorithm for enhancing the
performance of GPS/INS integration navigation system in heavy
jamming and high dynamic environments, that utilizes the GPS
carrier phase information to determine highly Time Space Position
Velocity Information (TSPVI).
[0036] Another objective of the present invention is to provide a
fully-coupled positioning process and system thereof, in which an
advanced fully-coupled GPS/INS integrated system architecture is
developed, which makes possible the implementation of mutual error
compensation and aiding between GPS and IMU from the view of
hardware and software implementation. The architecture provides the
most cost-efficient approach for the implementation of
hardware/software systems and the aiding of GPS with INS data.
[0037] Another objective of the present invention is to provide a
fully-coupled positioning process and system thereof, in which a
novel V-A (velocity-acceleration) aiding GPS signal tracking loop
algorithms including code tracking loop algorithm and carrier
tracking loop algorithm have been completed. Under the new
architecture of GPS/INS integration, both the GPS receiver's code
and carrier tracking loop can be aided by INS data at a high rate
of data, which dramatically increase the measurement accuracy,
dynamic tracking capability, and anti-jamming capability of GPS
receiver.
[0038] Another objective of the present invention is to provide a
fully-coupled positioning process and system thereof, in which an
innovative IMU aiding widelane carrier phase ambiguity resolution
on-the-fly algorithm is developed, which can provide highly and
precise carrier phase measurements for the integrated navigation
Kalman filter. The approach significantly reduces the time spent
for an ambiguity search procedure and increases the resolution of
ambiguity.
[0039] Another objective of the present invention is to provide a
fully coupled positioning process and system thereof, in which a
robust integrated navigation Kalman filter is implemented in real
time. The filter more effectively utilizes all available
measurements and a prior information, including GPS pseudorange,
delta range, carrier phases measurements, inertial measurement
information, to determine and correct for system errors in a
fully-coupled fashion.
[0040] Another objective of present invention is to provide a
fully-coupled positioning process and system thereof, in which a
novel algorithm for rapid transfer alignment and calibration for
both aircraft INS and munition INS attitude is studied. This
algorithm is used to remove initial position, velocity and attitude
errors of tactical munitions.
[0041] Another objective of present invention is to provide a
fully-coupled positioning process and system thereof, in which a
real-time kinematic GPS/IMU integrated navigation software system
is implemented, which also provide a tool for development of
different levels of GPS/IMU integrated navigation systems adaptable
to wide usage applications.
[0042] Another objective of present invention is to provide a
fully-coupled positioning process and system thereof, in which a
navigation computer system is designed, that directly points to a
broad class of military/civilian/government applications including
strike weapons, unmanned airborne vehicle and avionics
platforms.
[0043] Another objective of present invention is to provide a
fully-coupled positioning process and system thereof, which not
only provides a solid basis and powerful tools for the improvement
of accuracy of the navigation systems used for the guided vehicle,
but also creates a new trend and open new directions for further
investigation of challenging problems faced by designs of advanced
navigation systems for high dynamic vehicle.
[0044] Accordingly, in order to achieve the above objectives, the
following innovative technical features have to bring to our
investigation.
[0045] 1. Optimal Integrated Mode: Position and velocity (P-V)
integrated method, pseudorange and delta range
(.rho./.DELTA..nu.+.DELTA..theta.) integrated mode without IMU
aiding the GPS tracking loops, kinematic integration mode
.rho.+.phi./.DELTA..nu.+.DELTA..theta. without IMU aiding the GPS
tracking loops, (.rho./.DELTA..nu.+.DELTA..theta.) integrated mode
with IMU aiding the GPS tracking loops and
.rho.+.phi./.DELTA..nu.+.DELTA..theta. with IMU aiding the GPS
tracking loops. The comparison of the existing different
integration approaches led to the option of the optimal integration
architecture for the fully-coupled kinematic GPS/IMU integrated
navigation system.
[0046] 2. Innovative technique for inertial aiding of the GPS
tracking loops: One of the technology trends towards GPS/IMU
integrated systems is to develop a fully-coupled kinematic GPS/IMU
integrated system, where the GPS receiving set's code and carrier
tracking loops are aided with inertial sensor information. We have
tried to make use of the most updated results in our IMU aiding GPS
tracking loops algorithm to improve GPS measurement accuracy and
anti-jamming capability in a tactical dynamic environment.
[0047] 3. Novel inertial information aiding phase ambiguity
resolution technique: High dynamic kinematic GPS navigation is
limited by the ability to resolve the carrier integer cycle
ambiguity in a timely manner. An IMU aiding widelane ambiguity
resolution technique is utilized to significantly reduce the time
for the ambiguity search procedure and to obtain highly reliable
ambiguity solutions in a high dynamic environment. The method can
also be used for the resolution of carrier integer cycle ambiguity
in the single-frequency kinematic GPS measurements.
[0048] 4. Robust centralized integrated Kalman filter: A complete
approach to reliable, robust, and adaptable Kalman filter is
developed which can operate in more than one dynamic environment to
predict the actual system performance. This type of filter
configuration has many advantages over the usual Kalman filter such
as a larger region of convergence, smoother transitions between
over-determined solutions and more conservative modeling when
certain states are frozen, such as during clock or altitude hold.
In addition, the centralized filter approach to kinematic GPS/IMU
integrated algorithm avoids filter instability problems as the
filter-driving-filter configuration meets.
[0049] 5. Rapid transfer alignment.
[0050] Furthermore, a highly challenging research topic in the
development of a new generation integrated guidance navigation
systems using low-cost IMU sensors is to develop a fully-coupled
kinematic GPS/IMU navigation system adaptable to a high dynamic
flight environment. The term fully-coupled means that the IMU and
GPS directly complement each other. The IMU-derived velocity and
acceleration (V-A) information can be used to aid a GPS receiver's
code and carrier phase locked-loops for tracking the
Doppler-drifted satellite signals. And vice-versa, the long-term
stability and accuracy of the GPS position and velocity information
can be utilized to compensate and calibrate the bias and drift
errors of the IMU sensor. Several possible levels of hardware and
software integration methods have currently been presented for the
various purposes of GPS/IMU integration. According to the
traditional category method, the architecture of the integration
system is classified into two types: loosely-coupled and
tightly-coupled systems. Generally speaking, the loosely-coupled
system has an unambiguous definition and requirements for GPS and
INS. For example, the loosely-coupled system needs the independent
navigation solutions from GPS and INS systems, respectively. But,
the tightly-coupled system is easily confused from the view of
availability of the GPS and IMU measurement information. For
example, fusion of either pseudorange or carrier phase measurements
into the integrated Kalman filter leads to different requirements
for the GPS receiver and different integrated algorithms for data
processing. The information flow between the IMU and the GPS
receiver depends on levels of the GPS/IMU integration. Therefore,
we classify the integrated GPS/IMU system into 5 types of
integration modes from the view of information fusion.
[0051] A. GPS/INS P-V integration mode: Traditionally, it is also
called the loosely-coupled mechanization. In the integrated system,
the GPS and INS are considered as independent navigation systems,
as shown in FIG. 1.
[0052] The integrated navigation solutions are provided by a
separately integrated navigation Kalman filter, which directly
utilizes the navigation solutions (position and velocity or time
and attitude) derived by the GPS and INS navigation systems,
respectively. The GPS P-V solution can correct the INS solution
errors periodically. Theoretically, the IMU-derived V-A solution
can aid the GPS receiver tracking loops if the GPS receiver
hardware/software systems are properly designed. But, it is
practically difficult because the loosely-coupled mechanization has
a cascaded filter performance with which the integrated navigation
Kalman filter can not provide the GPS tracking loops with a high
rate data input. The GPS/INS P-V integrated navigation systems can
be found in military GPS applications in the past decades. One
disadvantage of the GPS/INS P-V integration system is that cascaded
filter performance can be degraded by correlations in the data.
Care must be taken to ensure that the time-correlated outputs of
the GPS filter do not cause stability problems in the integrated
navigation filter. Another disadvantage of the loosely coupled
architecture is that the GPS filter can experience large errors in
the presence of high receiver dynamics; this may necessitate aiding
from the integrated navigation filter, which can worsen the
correlation problem. But, an obvious advantage of the loosely
coupled technique is that it allows maximum use of off-the-self
hardware and software that can be easily assembled into a cascaded
system without major new development.
[0053] B. GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. Integration Mode
without Aiding of GPS Tracking Loops: In the GPS/IMU
.rho./.DELTA..nu.+.DELTA..theta. integration mode, the integrated
navigation Kalman filter directly fuses and processes the raw
measurement data from the GPS and IMU sensors, respectively, such
as GPS pseudorange (PR) and delta-range (DR) measurements, and
inertial indications of IMU acceleration and angular rate.
Therefore, the centralized navigation filter gives the unique
navigation solutions. FIG. 2 describes the architecture of the
integration mode.
[0054] The integration mode can dramatically improve the accuracy
of the integrated navigation system better than as the
loosely-coupled mode does. But, the integration mode can not
enhance the dynamics of GPS tracking loops and increase
anti-interference capability of the GPS receiving set because of
lack of the inertial aiding information available to the GPS
receiver tracking loops. Its main advantage is that almost all GPS
receivers in the market can be conveniently integrated with IMU
sensors into a GPS/IMU integrated navigation system, and there are
no special requirements for GPS receivers.
[0055] C. GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. Integration Mode
with Aiding of GPS Tracking Loops:
[0056] Traditionally, this integration mechanization is called the
tightly-coupled architecture. The obvious distinctions between the
above mode in B and this mode are the levels of information fusion
and requirements for the GPS receiver. This mode requires that the
GPS receiving set must accept the aiding information from the
integrated navigation Kalman filter for the GPS receiver code
tracking loop aiding. And the Kalman filter must output velocity
and acceleration (V-A) information at a high data rate in order to
allow the aiding information to be available in the GPS receiver.
FIG. 3 shows the architecture of this integration mode.
[0057] The integration architecture more effectively utilizes GPS
and IMU measurements and a priori information to determine and
correct for system errors in a highly integrated fashion. It also
yields better performance than the above two systems, providing
more accurate navigation estimates during periods of a high dynamic
flight or jamming environment. In the integration mode, the design
of a tracking loop mechanism for GPS sensors imposes the
requirements on both the hardware and the software algorithms for
reception and processing of the velocity and acceleration (V-A)
aiding information from the navigation filter. Its main advantages
are the enhancement of anti-jamming capability and improvement of
adaptability for high dynamic environments of the GPS receiving
set. From the view of the system design, the integrated navigation
system design faces more challenges in GPS receiver digital signal
processing, tracking loop aiding, data exchange and system
integration.
[0058] D. GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA..theta.
Integration Mode without Aiding of GPS Tracking Loops:
[0059] The integration mode is similar with the architecture in the
above mode B. The difference, however, is only in the type of
information fusion as shown in FIG. 4. In the integration mode, the
kinematic GPS technique is utilized in order to obtain more
accurate GPS measurement data.
[0060] The GPS carrier phase measurement can obtain the
sub-centimeter measurement accuracy. But, the phase integer cycle
ambiguity and cycle slip problems in the carrier phase observable
limit the obtainment of highly accurate positioning solutions,
especially for on-the-fly phase ambiguity resolution and cycle slip
detection in a high dynamic environment. Once the phase ambiguity
and cycle slip problems are solved, the integrated system can
achieve better positioning accuracy than the above other systems
can do. In addition, the integrated system has no special
requirements for the GPS receiver except for the carrier phase
measurement. Main disadvantage is that the integrated mode can not
improve the original dynamic performance of the GPS receiver.
[0061] E. GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA.74 Integration
Mode with Aiding of GPS Tracking Loops: We call this integration
mode the fully-coupled integration mode. In the fully-coupled
.rho.+.phi./.DELTA..nu.+.DELTA..theta. integration mode, all
available GPS measurements are integrated into a centralized
navigation Kalman filter with the IMU measurements. The integrated
velocity and acceleration information is also used to aid the GPS
receiver's code and carrier phase tracking loops in order to
improve the anti-interference capability and dynamics of the GPS
receiving set in a tactical high dynamic flight environment. It is
now a challenging problem to develop the kinematic GPS/IMU
integrated navigation system with the above integration
features.
[0062] This integration mode is the primary challenge in the
research and development of various integrated navigation systems.
The accuracy, reliability, dynamic performance and anti-jamming
capability of the integrated navigation system are dramatically
enhanced through use of the integration mechanism.
[0063] In the past years, some kinematic integration methods used
in GPS/IMU navigation systems are effective under a low dynamic
environment or limited flight environment, for example, in marine
navigation and aerial photographic aircraft. However, many
kinematic GPS/INS integration systems with the architecture in D
only consider how phase ambiguity and carrier cycle slips are
resolved and detected. But they do not further consider how IMU
velocity and acceleration information can be utilized for aiding
the GPS receiver tracking loops (Delay-Locked Loop or code loop/DLL
and Phase-Locked Loop/PLL). Unfortunately, such integration methods
do not improve the dynamic tracking capability of the GPS receivers
although they increase the accuracy of the integrated navigation
system and provide IMU instrument errors compensation during the
period of GPS receiver operation. In such integration technique,
the IMU only provides the estimated position and velocity for the
GPS receiver to reduce the phase ambiguity search space, and
ranging errors corrections in order to increase the navigation
accuracy. Once the GPS receiver loses its lock-onto satellite
signals, there is no further link between the GPS receiving set and
the IMU sensors.
[0064] Each one of the above integration modes has its advantages
in the corresponding performance/cost and synergy efficiency
trade-offs, flexibility and simplification of the realization and
redundant navigation solutions. But, available observables used in
the current integration methods are mainly code pseudorange and
range rate in addition to general acceleration and angular rate
outputs of IMU sensors. The velocity information derived by IMU
sensors is mainly utilized to aid the frequency lock-on of the
carrier-frequency tracking loop (Doppler removal before codes
matching) for the purpose of code delay measurement but not that of
carrier phase measurement. Therefore, the range measurement
accuracy is still limited by code tracking loop bandwidth and
resolution. Especially, in order to acquire the GPS satellite
signals in the high dynamic environment, the bandwidth of the
closed loop of the unaided carrier-phase tracking loop in the GPS
receiver must be wide enough to adapt to the fast GPS signal
frequency and phase changes caused by high dynamic motion. It is
fairly difficult to achieve without external aiding tracking
information because there exists unwanted interference noise which
will simultaneously enter the tracking loop with a wider
closed-loop bandwidth.
[0065] Moreover, a fully-coupled kinematic GPS/IMU algorithm
(FCKGA) navigation software package is incorporated the present
invention which efficiently utilizes the developed results in this
invention, such as robust centralized Kalman filter, IMU-aided
on-the-fly widelane ambiguity resolution and IMU V-A aiding
tracking loops.
[0066] The successful development of the FCKGA navigation software
system will give e a competitive edge with sophisticated navigation
and guidance systems. This advanced system is featured with the
following important advantages:
[0067] (1) Hardware-Level Redundancy: In the fully-coupled
integration mode, the GPS receiving set is used as one of the
sensors (GPS, gyro and accelerometer) of the integrated navigation
system. The restriction of at least 4 satellites for navigation
solution computation can be significantly relaxed. The
hardware-level redundancy will help to enhance the reliability of
the overall system through fault-tolerant software design.
[0068] (2) Use of Low-Cost IMU Sensors: In the FCKGA-based system,
precise positioning results can be used to routinely correct IMU
instrument errors in flight. Therefore, low-cost non-INS-grade
inertial sensors can be utilized in the integrated navigation
system.
[0069] (3) Minimum of Tracking Loop Bandwidth and High
Anti-Interference: In the fully-coupled integration mode, the V-A
solution of the integration navigation filter is transferred as V-A
information along the line-of-sight between the GPS satellites and
the integrated system, and fed back to the digital signal processor
of the GPS receiving set at a high rate. In the signal processor,
the V-A information is used to compensate the high dynamics.
Therefore, the fixed bandwidth of the tracking loop can be reduced
to a minimum to prevent unwanted interference noise.
[0070] (4) Fast Phase Ambiguity Resolution/Cycle Slip Detection
On-The-Fly: The precise positioning results of the integrated
system can generate the computed range between satellites and the
navigation system. The computed range is compared with the measured
range between the satellites and the navigation system, and the
resultant difference can be utilized to detect cycle slip and
reduce the ambiguity search space efficiently.
[0071] (5) High Navigation Accuracy: The integrated navigation
system uses the kinematic GPS technique with centimeter-level
measurement accuracy to significantly improve the navigation
accuracy of the integrated system. Once atmospheric delays and
selective availability (using dual-frequency and an authorized GPS
receiving set), phase ambiguity and cycle slip problems (using
methods developed in this invention) are solved, the navigation
accuracy of the integrated system only depends on the IMU
instrument errors. The integrated navigation system is designed to
perform IMU dynamic correction and alignment. Furthermore, the
integrated navigation output is at the rate of the INS output.
BRIEF DESCRIPTION OF DRAWING
[0072] FIG. 1 is a block diagram illustrating the architecture of
GPS/INS P-V integration mode.
[0073] FIG. 2 a block diagram illustrating the architecture of
GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. Integration Mode without
Aiding of GPS Tracking Loops.
[0074] FIG. 3 is a block diagram illustrating the architecture of
GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. Integration Mode with
Aiding of GPS Tracking Loops.
[0075] FIG. 4 is a block diagram illustrating the architecture of
GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA..theta. Integration Mode
without Aiding of GPS Tracking Loops.
[0076] FIG. 5 is a block diagram illustrating the architecture of
GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA..theta. Integration Mode
with Aiding of GPS Tracking Loops.
[0077] FIG. 6 is a block diagram illustrating the DPLL with the INS
aiding.
[0078] FIG. 7 is a block diagram illustrating the IMU aiding code
tracking model.
[0079] FIG. 8 is a block diagram illustrating the architecture of
the IMU aiding on-the-fly phase ambiguity resolution.
[0080] FIG. 9 is a block diagram illustrating the inertial
navigation solution processing.
[0081] FIG. 10 is a block diagram illustrating the integrated
Kalman filter structure.
[0082] FIG. 11 is a block diagram illustrating the fully-coupled
positioning system structure.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0083] The present invention provides a process and system for
fully-coupled positioning of a vehicle on the air, land, and space,
using the measurements from a global positioning system and an
inertial measurement unit to improve navigation performance.
[0084] Referring to FIG. 5, the fully-coupled positioning process
of the present invention comprises the steps as follows:
[0085] 1. Accept an angular rate and acceleration measurements of a
vehicle from an inertial measurement unit or position, velocity,
and attitude measurements of other external sensors and compute an
initial value for IMU navigation equations;
[0086] 2. Receive GPS radio frequency signals and derive GPS
pseudorange, delta range, and carrier phase measurements;
[0087] 3. Receive the angular rate and acceleration information
from the inertial measurement unit and solve the inertial
navigation equations to obtain an inertial navigation solution,
such as position, velocity, and attitude.
[0088] 4. Combine the GPS pseudorange, the delta range, and the
carrier phase measurements and the inertial navigation solution for
obtaining a fully-coupled positioning solution.
[0089] To obtain better performance, in step 2, the GPS signal is
tracked by a carrier phase locked loop and a code tracking loop of
the GPS receiver, and the process of the phase locked loop and code
tracking loop of the GPS receiver can be aided by the obtained
fully-coupled positioning solution from the step 4.
[0090] To obtain better performance, in step 3, the errors of the
inertial navigation solution can be removed with the optimal
estimates of errors of the inertial navigation solution from the
step 4.
[0091] To obtain better performance, the step 2 further comprises a
step 2A of performing carrier phase integer ambiguity resolution
and cycle slip detection processing to incorporate the high
accurate GPS carrier phase measurements to the combination of the
step 4.
[0092] To obtain better performance, the step 2 further comprises a
step 2B, which aids processing of carrier phase integer ambiguity
resolution and cycle slip detection with fully-coupled positioning
solution from the step 4.
[0093] To obtain better performance, the step 4 can be implemented
by a Kalman filter.
[0094] To obtain better performance, the step 4 can be implemented
by a multi-mode Kalman filter.
[0095] To obtain better performance, the step 4 can be implemented
by a robust Kalman filter.
[0096] The body angular rate and acceleration measurements from the
IMU 5 are processed in the navigation solution 40, and are passed
to the integrated Kalman filter 60. The error estimates provided by
the integrated Kalman filter 60 are fed back to the navigation
solution 40 to remove the errors of the position, velocity and
inertial sensors. The GPS signals received by a GPS antenna are
processed in the GPS RF/IF Unit 10, the carrier and code tracking
loops 20, the ambiguity resolution 50 and the message decoding 30,
and are passed to the integrated Kalman filter 60. The optimal
velocity and acceleration information of the integrated Kalman
filter 60 are fed back to the carrier and code tracking loops to
aid GPS signal tracking process. The position, velocity, attitude
from the navigation solution 40 is input to the ambiguity
resolution 50 to aid the GPS carrier phase integer ambiguity
resolution.
[0097] In step 1, the initial position, velocity, and attitude
information need be provided to the navigation equations of the
navigation solution 40 during initializing process of IMU before
the integrated kinematic GPS/IMU system is put to formal operation.
The step 1 may have three methods to initialize an IMU:
[0098] 1-1) Self-initialization of IMU.
[0099] This mode can be just used on the static ground. The signals
input to the step 1-1 process is the gravity, Earth rate signal,
latitude, and longitude. The gravity signal the accelerator of the
IMU 5 senses is used to determinate local level plane. The Earth
rate signal the gyro of the IMU 5 senses is used to determinate
azimuth. The step 1-1 process output the position, velocity,
attitude, and estimated inertial sensor error to the navigation
equation of the navigation solution 40.
[0100] 1-2) Transfer Alignment.
[0101] If the IMU is a tactical munition IMU installed in a launch
platform, the step 1-2 processes the measurement or navigation
parameter of the tactical munition IMU and the launch platform IMU
to align the tactical munition IMU. The measurement or navigation
parameter of the tactical munition IMU and the launch platform IMU
are input to a transfer alignment process. In the transfer
alignment process, a transfer alignment filter processes the
measurement or the navigation parameter to provide the optimal
initialization information to the tactical munition IMU.
[0102] 1-3) GPS in-flight alignment
[0103] If a global position system receiver is selected as the
external sensor, the GPS position and velocity, an inaccurate
initial attitude value are used first as an initial value of the
navigation equations of the inertial measurement unit, and accurate
alignment of the inertial measurement unit is further performed
using GPS signals. Referring to FIG. 5, the step 2 further
comprises the following steps:
[0104] Step 2-1: The received GPS signals are input to the GPS
RF/IF unit 10. In the GPS RF/IF unit 10, the GPS RF (radio
frequency) signals are amplified and down converted to IF
(Intermediate Frequency). The GPS IF signals are amplified,
low-pass filtered, and transformed onto GPS baseband signals. The
analog GPS baseband signals are sampled in an Analog-to-Digital
(A/D) converter. The digital GPS signals are passed to the carrier
and tracking loops 20.
[0105] Step 2-2: The digital GPS signals from the GPS RF/IF unit 10
and the V-A (velocity-accelerate) aiding information from the
Integrated Kalman filter 60 are input to the carrier and code
tracking loops 20, which comprises a carrier phase locked loop and
an early-late digital delay lock loop (DDLL) for a tracking GPS
satellite signals.
[0106] GPS pseudorange, delta range, and carrier phase measurements
are output by the carrier and code tracking loops 20 to the
ambiguity resolution 50. The recovered carrier and code and GPS
signal are output by the carrier and code tracking loops 20 to the
message decoding 30.
[0107] Referring to FIG. 5 and FIG. 6, the i-th sample of the
received GPS signal (for example, L1 C/A code) down-converted into
the baseband from the GPS RF/IF unit 10 is
s(i)={square root}{square root over
(2P)}CA[(1+.zeta.)iT.sub.s-.xi.T.sub.c-
]cos[(.omega..sub.h+.omega..sub.d)i+.phi..sub.0]+n(i)
[0108] Where
[0109] P is the received signal power.
[0110] CA[.cndot.] is a .+-.1-valued PRN code with rate R, and is
delayed by .tau.=.xi.T.sub.c with respect to the GPS signal
transmitting time (T.sub.c is the code chip width).
[0111] .omega..sub.b(=2.pi..function..sub.bT.sub.s),
.omega..sub.d(=2.pi..function..sub.dT.sub.s) are the sampled signal
angular frequencies corresponding to the baseband carrier frequency
.theta..sub.b and Doppler shift .function..sub.d (T.sub.s is the
sampling period).
[0112] .phi..sub.0 is the initial value of the signal carrier phase
at i=0.
[0113] n(i) is the equivalent input Gaussian band-limited noise at
the baseband.
[0114] The code rate R is equal to (1+.zeta.)R.sub.0, where
.zeta.=.function..sub.d/.function..sub.c (.function..sub.c is the
carrier frequency) corresponding to the code Doppler shift of
(.function..sub.d/.function..sub.c)R.sub.0, and R.sub.0 is the code
rate without the Doppler shift.
[0115] The GPS digital signal from the GPS RF/IF unit 10 are
multiplied with the local in-phase cos {circumflex over (.theta.)}
and quadrature sin {circumflex over (.theta.)} reference signals
from the carrier NCO 2010 at the mixer 201 and mixer 203,
respectively. The outputs of the mixer 201 and mixer 203 are
multiplied by the prompt PRN (pseudo random noise ) code from the
code generator 2017 of the code tracking loop at the mixer 202 and
mixer 204, respectively, in order to remove the input PRN code
modulated on the carrier frequency that is called despreading
operation. After the pseudo random noise code is removed, the GPS
signal SNR (signal-noise ratio) is increased by the despreading
gain provided by the despreading operation. The output of the mixer
202 is passed to the accumulator 206 to be accumulated. The output
of the mixer 204 is passed to the accumulator 205 to be
accumulated. The in-phase signal output by the accumulator 206
is
I(k)={square root}{square root over (P/2)}R(.tau.-{circumflex over
(.tau.)})sin c[(.DELTA..omega..sub.d).sub.kN/2]cos.left
brkt-bot..theta.(k)-{circumflex over (.theta.)}(k).right
brkt-bot.+n.sub.l(k)
[0116] which is output to the carrier phase discriminator 207. The
quadrature signal output by the accumulator 205
Q(k)={square root}{square root over (P/2)}R(.tau.-{circumflex over
(.tau.)})sin c[(.DELTA..omega..sub.d).sub.kN/2]sin.left
brkt-bot..theta.(k)-{circumflex over (.theta.)}(k).right
brkt-bot.+n.sub.Q(k)
[0117] to the carrier phase discriminator 207.
[0118] Where,
[0119] (.DELTA..omega..sub.d).sub.k=.omega..sub.dk-{circumflex over
(.omega.)}.sub.dk is the Doppler shift estimation error in the k-th
interval.
[0120] .theta.(k), {circumflex over (.theta.)}(k) are the phases of
the incoming signal and the local NCO signal at the center of the
interval, respectively.
[0121] n.sub.l(k), n.sub.Q(k) are the lumped noise components of
I(k) and Q(k), respectively.
[0122] The carrier phase discriminator 207 works on the outputs of
the in-phase and quadrature correlators at a rate
(.function..sub.s/N), where .function..sub.s the sampling rate and
N is the number of data samples in each DDLL correlation
interval.
[0123] The arctangent phase detection operation of the in-phase and
quadrature signals in the carrier phase discriminator 207 results
in the phase error, as follows
e(k)=arctan[Q(k)/I(k)]=g[.epsilon.(k)]+n.sub..theta.(k)
e(k).di-elect cons.[-.pi.,+.pi.]
[0124] where
[0125] g[.cndot.] is the characteristic function of the phase
discriminator.
[0126] .epsilon.(k)=.theta.(k)-{circumflex over (.theta.)}(k) is
the phase tracking error due to a noise-free incoming signal.
[0127] n.sub..theta.(k).di-elect
cons.(-.pi.-g[.epsilon.(k)],+.pi.-g[.epsi- lon.(k)]) is the phase
disturbance due to the input noise.
[0128] As a result, the characteristic curve of the carrier phase
discriminator 207 is linear with a period 2.pi.,
g[.epsilon.(k)]=.epsilon.(k)mod[-.pi.+.pi.]
[0129] The phase tracking error is output by the carrier phase
discriminator 207 to the loop filter 208. The loop filter 208 is a
digital filter and generally is implemented in first-order, or
2.sup.nd-order, or 3.sup.rd-order. The noise of the phase tracking
error is filtered by the loop filter 208 filters, and the filtered
phase tracking error is output by the loop filter 208 filters to
the adder 209.
[0130] The phase tracking error form the loop filter 208 is
received by the adder 209 receives, and is added with the IMU
aiding data from the integrated Kalman filter 60, and is input to
the carrier NCO (numerically-controlled oscillator) 2010.
[0131] The local in-phase and quadrature reference signals at the
sampling rate .function..sub.s for GPS non-coherently correlating
(in the carrier phase acquisition process) or coherently
correlating (in the carrier fine tracking process) generated by the
carrier NCO 2010 are adjusted using the input phase tracking
error.
[0132] The local in-phase signal is output by the carrier NCO 2010
to the mixer 201 and quadrature reference signals is output by the
carrier NCO 2010 to the mixer 203, and GPS delta rang and carrier
phase measurements are output by the carrier NCO 2010 to the
ambiguity resolution 50.
[0133] Referring to FIG. 7, cooperated with the carrier phase
locked loop, an early-late digital delay lock loop (DDLL) for a
tracked GPS satellite, which is aided by IMU data, is used to
perform two purposes:
[0134] (1) The code tracking loop provides an estimate of the
magnitude of time shift required to maximize the correlation
between the incoming signal and the receiver's internally generated
`on-time` code; this information of time delay is generally used by
the receiver to calculate an initial user-to-satellite range
estimate, known as the pseudorange (PR) measurement.
[0135] (2) The synchronized replica code signal derived from the
tracking operation is applied to despread the GPS signal; this
despread signal is then passed to the receiver's carrier tracking
loop for demodulation of the data message, D(t), and for the
carrier phase tracking process.
[0136] The typical GPS receiver uses the standard non-coherent
delay-locked loop (NCDLL) as its code tracking loop. This loop is
often called the `early-late` delay-lock loop, referring to the
advanced and delayed versions of the code replica driven by the
voltage-controlled-oscillator. The coherent DLL is also used in
some of the GPS receivers, which requires parallel carrier-phase
tracking (and thus, the coherent qualifier). The most disadvantage
inherent with the coherent DLL is that the code tracking loop will
break lock when taking into account bit error or cycle slips. This
is because the coherent DLL only works when phase tracking is
successful. The coherent DLL only applies to those applications
that also require successful phase tracking. Here only the process
of the NCDLL with IMU aiding information is presented.
[0137] The outputs of the mixer 201 and the mixer 203 and the
"Early" and "late" local codes from the code generator 2017 are
received by the correlator 2011, and are put to the correlating
computations. The results of the correlation in correlator 2011 are
input to the DLL discriminator 2012.
[0138] The results of the correlation in the correlator 2011, which
is the function of the code phase tracking error, accepted by the
DLL discriminator 2012, and are used to extracts the code phase
tracking error. The extracted code tracking estimation error from
the DLL discriminator 2012 is input to the low pass filter 2013 to
filter out the noise in the code phase tracking error.
[0139] The code phase tracking error, which is from the DLL
discriminator 2012 and is corrupted by incoming noise, received by
the low pass filter 2013, and are filtered. The filtered code phase
tracking error is output to the adder 2017. The low pass filter
2013 is a digital filter and generally is implemented in
first-order, or 2.sup.nd-order, or 3.sup.rd-order.
[0140] The code phase tracking error from the low pass filter 2013
is added by the adder 2014 with the Doppler aiding from IMU data
through the integrated Kalman filter 60.
[0141] The output of the adder 2014 is added by the adder 2015 with
the normal chipping rate, and is output to the code NCO 2016.
[0142] The PRN code generated by the code NCO 2016 is output to the
code generator 2017.
[0143] The local "Early", "late", and prompt code generated by the
code generator 2017 and are output to the correlator 2011. The GPS
pseudorange measurements measured by the code generator 2017 are
output to the ambiguity resolution 50.
[0144] After the processing of the carrier phase locked loops and
the code late lock loops, the carrier and code of the received GPS
signal are recovered. The received GPS baseband signal from the
GPS/IF unit 10, the recovered code and carrier from the carrier and
code tracking loops 20 are passed to the message decoding 30.
[0145] The GPS ephemeris from the received GPS baseband signal are
demodulated by the message decoding 30 and are passed to the
integrated Kalman filter 60.
[0146] Referring to FIG. 8, in step 2A and 2B, more accurate
positioning with GPS is obtained by use of carrier phase
measurement than by use of pseudorange measurements. This is
because at the satellite L1 broadcast frequency, 1575.42 MHz, one
cycle of the carrier is only 19 cm as compared to that of one cycle
of the C/A code which is around 300 m. The high accuracy of
positioning with GPS carrier phase measurement is based on the
prior condition that the phase ambiguities have been resolved. The
ambiguity inherent with phase measurements depends upon both the
receiver and the satellite. Under the ideal assumptions of no
carrier phase detection error and the known true locations of the
receiver and satellite, the ambiguity can be resolved
instantaneously through a simple math computation. However, there
is the presence of satellite ephemeris error, satellite clock bias,
atmospheric propagation delay, multipath effect, receiver clock
error and receiver noise in range measurements from GPS code
tracking loop, we can only get a non-precise geometric distance
from the receiver to the satellite which is called a code
pseudorange.
[0147] The advantage of the IMU aiding phase ambiguity resolution
and cycle slip detection is that the precision vehicle coordinates
and velocity from the corrected INS solution are available to aid
in determining the original ambiguities and the search volume.
Additionally, the INS aiding signal tracking enhances the
receiver's capability to hold the GPS signal, thus the probability
of signal loss or cycle slip will be reduced.
[0148] The carrier frequency of GPS signals transmitted by GPS
satellites may be divided into two carrier frequency L1 an L2, a
GPS receiver can receive single frequency or dual frequency signal
dependant on its design. The multi-mode ambiguity resolution
algorithm 504 consists of the following modes:
[0149] (1) resolving ambiguities with single frequency phase
data;
[0150] (2) resolving ambiguities with dual frequency phase
data;
[0151] (3) resolving ambiguities by combining dual frequency
carrier phase and code data, under the aiding of a GPS/IMU
integrated filter.
[0152] When dual frequency is unavailable, the single frequency
data and the geometrical distance derived from the data of the
integrated Kalman filter 60 and the satellite prediction algorithm
502 are used to resolve the phase ambiguities. When dual frequency
data is available, the wide lane technique is applied to extract
the wide lane ambiguities. With the INS aiding, the wide lane
ambiguities can be fixed quickly.
[0153] Referring to FIG. 8, the outputs of the inonospheric mode
and troposheric mode and satellite clock model 501, the outputs of
the satellite prediction algorithm 502, the outputs of the
multi-mode cycle slip detection and repair algorithm 503, the
output of the integrated Kalman filter 60, and the outputs of the
carrier and code tracking loops 20 are received by the multi-mode
ambiguity resolution 504 to resolve the carrier phase integer
ambiguity.
[0154] After resolving the carrier phase integer ambiguity, GPS
pseudorange, delta range, and carrier phase measurements are passed
by the ambiguity resolution 50 to the integrated Kalman filter
60.
[0155] The GPS ephemeris from the message decoding 30 through the
integrated Kalman filter 60 are received by the satellite
prediction algorithm 502 and are used to compute the GPS satellite
position and velocity, which are input to the multi-mode ambiguity
resolution 504.
[0156] The effects of ionospheric and troposheric propagation of
GPS signal and GPS satellite clock errors are modeled by the
ionospheric model and troposheric model and satellite clock model
501. The delay of ionospheric and troposheric propagation of GPS
signal and GPS satellite clock errors are computed by the
ionospheric model and troposheric model and satellite clock model
501 and are input to the multi-mode ambiguity resolution 504.
[0157] The GPS receiver position and GPS satellite position from
the integrated Kalman filter 60 are received by the multi-mode
cycle slip detection and repair algorithm 504 receives, which
includes the cycle slip detection algorithms and repair algorithms.
The cycle slip detection algorithm is composed of a number of
testing quantities. The first one is the measured carrier phase.
This method often leads to a failure of cycle slip detection
because phase is disturbed by many time dependent errors. The
second method used for cycle slip detection is a dual-frequency
phase combination. The phase/code combination is also used to
determine the cycle slip in this multi-mode cycle slip detection
algorithm. The last one is utilizing the velocity information from
the integrated Kalman filter 60 to detect and repair the cycle
slip. Once cycle slip occurring is detected by the multi-mode cycle
slip detection algorithm 504, the location of the carrier phase
jump and the size of cycle slip are determined. The repairs are
made though correcting all subsequent phase observations for this
satellite and this carrier phase by a fix amount. The repaired
carrier phase is output by the multi-mode cycle slip detection and
repair algorithm 503 to the multi-mode ambiguity resolution 504
after the location of the carrier phase jump and the size of cycle
slip are determined and repaired.
[0158] The GPS receiver position and velocity and the covariance of
the position error and velocity error from the integrated Kalman
filter 60 are input to the multi-mode ambiguity resolution 504 and
the multi-mode cycle slip detection and repair algorithm 503, in
order to extract the geometrical distance between the satellite and
the GPS receiver and to determine the ambiguities search
volume.
[0159] The GPS pseudorang, delta range, carrier phase measurements
from the carrier and code tracking loops 20 are input to the
multi-mode ambiguity resolution algorithm 504.
[0160] Referring to FIG. 9, in step 3, the body angular rates from
the IMU 5, which is sensed by the gyros of the IMU 5, is passed to
the attitude matrix computation 401.
[0161] The body angular rates from the IMU 5, and the rotation rate
vector of the local navigation frame (n frame) relative to the
inertial frame (i frame) from the navigation computation 404, and
the estimates of attitude errors from the integrated Kalman filter
60 are received by the attitude matrix computation 402 and are used
to update an attitude matrix and remove attitude errors. The way to
update attitude matrix is Euler method, or the direction cosine
method, or the quaternion method.
[0162] The attitude matrix is output by the attitude matrix
computation 401 to the attitude angle computation 403 and is used
to extract the pitch, roll, and yaw angle from the attitude matrix,
which is the part of the INS solution.
[0163] The acceleration from the accelerators of the IMU 5, which
is expressed in the body frame (b frame), and the attitude matrix
from the attitude matrix computation 401 are received by the
attitude matrix 402. The input acceleration expressed in body frame
is transformed by the attitude matrix 401 to the acceleration
expressed in the navigation frame (n frame), which is output to the
navigation computation 404.
[0164] The acceleration expressed in the navigation frame from the
attitude matrix 402 and the estimate of position errors and
velocity errors from the integrated Kalman filter are received by
the navigation computation 404, and are used to compute the
position and velocity, and to remove the errors of the position and
velocity, and to compute the rotation rate vector of the local
navigation frame (n frame) relative to the inertial frame (i
frame).
[0165] The position and velocity as part of INS solution are output
by the navigation computation 404 to the integrated Kalman filter
60. The rotation rate vector of the local navigation frame (n
frame) relative to the inertial frame (i frame) is output by the
navigation computation 404 to the attitude computation 401.
[0166] It is well-known that the real-time Kalman filter produces
optimal estimates with well defined statistical properties. The
estimates are unbiased and they have minimum variance within the
class of linear unbiased estimates. The quality of the estimates is
however only guaranteed as long as the assumptions underlying the
mathematical model hold. Any misspecification in the model may
invalidate the results of filtering and thus also any conclusion
based on them. It is therefore very important to have novel ways to
verify the validity of the assumed mathematical model. Especially
in the GPS/IMU integrated navigation system applications, the key
to successful development of a real-time Kalman filter is to
generate a filter model of adequate size to predict the actual
system performance and at the same time operate within the
processor throughput constraints. In the fully-coupled kinematic
GPS/IMU algorithm (FCKGA) design, the filter must also be robust
enough to operate in more than one dynamical environment because
the integrated navigation system based on the FCKGA algorithm will
be applied to a wide class of military/civilian moving platforms,
such as tactical munitions, unmanned airborne vehicles, smart
bombs, precision strike weapons and avionics platforms. If the
dynamics change drastically, or if a sensor failure occurs, for
example, a GPS satellite signal failure or an inertial sensor
signal failure, the filter must detect, rectify and isolate the
failure situation, and finally reconfigure the integrated
navigation algorithm by use of some mechanism.
[0167] These notions or concepts of filter integrity and robustness
are fully coupled even though they are generally considered
independently. A robust filter has the characteristic that it
provides near-optimum performance over a large class of process and
measurement models. The Kalman filter is not robust since it is
optimal for only one particular process and measurement model. If
the filter is not correct the filter covariance may report
accuracies which are different from what can actually be achieved.
The purpose of filter integrity is to ensure that the predicted
performance from the error covariance is close to the actual
estimation error statistics.
[0168] In addition, filter divergence is usually caused by a
changing process or measurement model or a sensor failure. Residual
monitoring schemes can efficiently detect hard and soft failures
and filter divergence. One benefit of the residual monitoring
approach is that when the filter model is correct, the statistical
distribution of the residual sequence is known. Thus, it is easy to
generate a measurement editing and divergence detection scheme
using a test-of-distribution on the measurement residuals. The same
statistics can be used to assess the filter tuning and adjust the
size of the covariance when divergence is detected.
[0169] Since the residual statistics are normalized by the
projection of the state error covariance onto the measurement
geometry, the error covariance must be accurate, or at least
conservative, in order to maintain a reasonable false alarm rate.
This requires that all known measurement errors be properly modeled
and that the process model be fairly accurate. A reduced-order
filter known as the Schmidt-Kalman filter is applied which allows
certain states that are known to be present to be considered
without being estimated. This filter leads to accurate
uncertainties on the states which are estimated. This filter also
has a wider region of convergence since certain non-linear effects
can be included in the measurement error model.
[0170] It is an important technical core in the FCKGA that the
development of a multi-mode integrated navigation Kalman
Filter/estimator with robustness performs IMU in-flight alignment
and error drift correction, and navigation solution using kinematic
GPS/IMU data. All raw measurements processed by data fusion and
failure detection and isolation techniques are sent into the
multi-mode integration Kalman filter together with some command
instructions. Therefore, several preprocessing functions in
addition to the multi-mode filter design must be performed first.
These functions include GPS/IMU failure detection, identification
and isolation so that reliable measurement data can be provided to
the multi-mode navigation filter. In turn the accurate output of
the multi-mode filter can be utilized to make corrections for
failure analyses.
[0171] Referring to FIG. 10, in step 4, robust statistics and
estimation are used for covariance analyses of the navigation
solution 606 in order to provide P-V-A aiding information, system
reconfiguration commands and reliable navigation solution. The
development of a novel algorithm for failure analysis can
efficiently detect, isolate and compensate satellite signal
failures or IMU sensor failures, and allow the system to provide
satisfactory navigation and guidance signals for the control of a
vehicle.
[0172] The forced mode select commands and filter parameters from
the human-machine interface are received and interpreted by the
commands 601. The meaning of the commands and parameters is output
by the commands 601 to the mode selection 603.
[0173] The GPS measurements form the ambiguity resolution 50, GPS
ephesians from the message decoding 30, and the INS solution from
the navigation solution 40 are received and passed by the data 602
to the mode selection 603.
[0174] The meaning of the forced mode select commands and filter
parameters and the all measurements of GPS and INS are received by
the mode selection 603. Based on the forced mode select commands
and filter parameters and the availability of the all measurements
of GPS and INS, a current operation mode of the fully-coupled
positioning is made by the mode selection 603. The corresponding
system model and measurement mode, and measurements of the filter
are passed by the mode selection 603 to the integration navigation
system reconfiguration 604. The operation modes which the mode
selection 603 may select are:
[0175] (1) GPS/INS P-V (position and velocity )integration
mode;
[0176] (2) GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. (pseudorange
and delta range) integration mode without aiding of GPS tracking
loops;
[0177] (3) GPS/IMU .rho./.DELTA..nu.+.DELTA..theta. (pseudorange
and delta range) integration mode with aiding of GPS tracking
loops;
[0178] (4) GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA..theta.
(pseudorange, delta range, and carrier phase) integration mode
without aiding of GPS tracking loops;
[0179] (5) GPS/IMU .rho.+.phi./.DELTA..nu.+.DELTA..theta.
(pseudorange, delta range, and carrier phase) integration mode with
aiding of GPS tracking loops.
[0180] The system model and measurement mode, and measurements of
the filter from the mode selection 603 are received by the
integration navigation system reconfiguration 604 and the discrete
operation of system model and linearizing of measurement model are
performed. The covariance matrix of system process is adjusted by
the integration navigation system reconfiguration 604 based on the
results of measurement residues monitoring from the covariance
analyses of navigation solution 606, and passed to the robust
Kalman filter for the navigation solution 605.
[0181] The discrete system model and linearized measurement model,
the adjusted covariance matrix of system process and the formed
measurements from the integration navigation system reconfiguration
604 are received by the robust Kalman filter for the navigation
solution 605.
[0182] Optimal estimates of the errors of INS navigation
parameters, inertial sensor, and GPS measurement, are made by the
robust Kalman filter for the navigation solution 605 using the
received data and passed to the navigation solution 40. The
navigation system solution and optimal estimates of INS navigation
parameter errors and GPS measurement errors are output by the
robust Kalman filter for the navigation solution 605 to the
ambiguity resolution 50. The measurements residues and the
covariance of the system process are output by the robust Kalman
filter for the navigation solution 605 to the covariance analyses
of navigation solution 606. The navigation solution such as
position, velocity, attitude, and time is output by the robust
Kalman filter for the navigation solution 605.
[0183] The robust Kalman filter is implemented in full-order Kalman
filter or reduced-order Kalman filter, such as Schmidt Kalman
filter.
[0184] If the robust Kalman filter for navigation solution 605 is
assigned to the GPS/INS P-V integration mode, a stand-alone GPS
solution processing is included in the robust Kalman filter for
navigation solution 605 to obtain GPS-derived position and
velocity. The stand-alone GPS solution processing may be
implemented with point solution algorithms or a Kalman filtering
algorithm.
[0185] The filter measurements residues and the covariance of the
system process from the robust Kalman filter for the navigation
solution 605 are received by the covariance analyses of the
navigation solution 606 receives, and are used to perform filter
divergence testing. Based on the results of the filter divergence
testing, the adjusting value of the covariance of the system
process are output by the covariance analyses of the navigation
solution 606 to the integration navigation system reconfiguration
604 to maintain the filter stability.
[0186] Referring to FIG. 11, the fully-coupled positioning system
of the present invention comprises the following devices:
[0187] (1) A GPS RF unit 801 for receiving the GPS RF signals.
[0188] (2) A GPS digital signal processing unit 802 for processing
incoming GPS signal and obtaining GPS measurements
[0189] (3) A IMU data sampling unit 806 for collecting the
measurements from a IMU
[0190] (4) A Centralized navigation Kalman filter 803 for receiving
and processing the measurements from the IMU and the GPS
[0191] (5) Interfaces 804 for realizing the data and control
communication with other avionics systems.
[0192] (6) A time sychronizer 805 for providing the time signal and
local reference signal to the other devices.
[0193] The GPS RF unit 801 receives the RF signals transmitted by
GPS satellites, and then amplifies and down converts them into the
IF signals. The GPS digital signal processing unit 802 digitizes
the IF signals, carries out DLL and PLL with external IMU V-A
aiding and decodes the navigation message implemented in a DSP
(digital signal processor) processor and outputs are raw
measurements and the navigation message. The centralized navigation
Kalman filter 803 is the heart of the integrated navigation system,
which performs the navigation solution by use of the robust Kalman
filtering technique and dynamically calibrates IMU errors. In
addition, the acceleration and velocity information is fedback to
the GPS digital processing unit 802 for aiding the GPS receiver
code and carrier tracking loops in order to improve the dynamics of
GPS receiving unit. The time sychronizer 805 is a frequency
reference providing a local RF reference for the GPS RF unit and
synchronous control frequency and signal for the GPS digital signal
processing unit 802 and the IMU Data sampling circuit 806. The IMU
data sampling circuit 806 is an IMU data sampling and converter
unit. The interfaces 804 realize the data and control communication
with other avionics systems.
[0194] The GPS RF unit 801 connects directly with the GPS digital
signal processing unit 802 and the time synchronizer 805 through a
connector, such as a cable, and including the following means:
[0195] A. A GPS antenna or multipy-GPS antenna to receive GPS
signals transmitted by GPS satellites.
[0196] B. A RF-IF converter, which is connected between the GPS
antenna and the GPS digital signal processing unit 802 and is
connected with the time synchronizer 805 to obtain local reference
signal, to convert down the GPS RF signal from the GPS antenna to
the GPS IF signal input to the GPS digital signal processing unit
802.
[0197] An amplifier may be connected between the GPS antenna and
the RF-IF converter to improve signal-noise ratio (SNR) of incoming
GPS signal.
[0198] The GPS Digital signal processing unit 802 is connected
between the GPS RF unit 801 and the Centralized navigation Kalman
filter 803. Meanwhile, the time synchronizer 805 is connected to
the GPS digital signal processing unit 802 to provide time signal.
The GPS digital signal processing unit 802 includes the following
means:
[0199] A. An A/D converter, which is connected between the RF-IF
converter of the GPS RF unit 801 and a DSP processor, to sample the
GPS IF signal from RF-IF converter of the GPS RF unit 801.
[0200] B. A DSP processor, which is connected between the A/D
converter and the centralized navigation Kalman Filter 803, to
track and process the GPS digital signal to obtain GPS measurements
input to the centralized navigation Kalman Filter 803.
[0201] An IF-Baseband converter may be connected between the RF-IF
converter of the GPS RF unit 801 and the A/D converter to further
convert down the GPS IF signal from the RF-IF converter of the GPS
RF unit 801 to the GPS baseband signal input to the A/D
converter.
[0202] The centralized navigation Kalman filter 803 is a
microprocessor, which is connect with the GPS digital signal
processing unit 802 and the interfaces 804 and the IMU data
sampling circuit 806, to process the GPS measurements form the GPS
digital signal processing unit 802 and IMU measurements from the
IMU data sampling circuit 806.
[0203] The interfaces 804 are connected with the centralized
navigation Kalman filter 803 to realize communication with other
avionics systems, and may have many types, including:
[0204] (1) Serial signal interface, including synchronous
communication interface and asynchronous communication interface,
such as RS-232 interface, RS-422 interface, RS-485 interface, and
etc.
[0205] (2) Parallel digital signal interface.
[0206] (3) Network adapter, such as NE2000 adapter.
[0207] (4) Bus interface, such as MIL-1553 interface and ARIC 429
interface.
[0208] The IMU data sampling circuit 806 is connected with the
centralized navigation Kalman filter 803 and the time synchronizer
805 and may have many types, includes
[0209] (1) D/A converter-based circuit, which is used to adapt an
IMU with analog output.
[0210] (2) Pulse/counter-based circuit, which is used to adapt an
IMU with pulse output.
[0211] (3) Serial digital signal communication-based circuit, which
is used to adapt an IMU with serial digital signal interface.
[0212] (4) Parallel digital signal communication-based circuit,
which is used to adapt an IMU with parallel digital signal
interface.
[0213] (5) Network adapter-based circuit, which is used to adapt an
IMU with network standard output.
[0214] (6) Bus interface-based circuit, which is used to adapt an
IMU with a bus standard output interface.
[0215] If the processing capability of the micro-processor of the
GPS digital signal processing unit 802 and the centralized
navigation Kalman filter 803 is enough big, the centralized
navigation Kalman filter 803 may be removed or the micro-processor
of the GPS digital signal processing unit 802 may be removed. Then,
the operation tasks of the centralized navigation Kalman filter 803
may be assigned to the micro-processor of the GPS digital signal
processing unit 802, or, operation tasks of the micro-processor of
the GPS digital signal processing unit 802 may be assigned to the
centralized navigation Kalman filter 803.
[0216] In developing advanced IMU, a microprocessor is embedded in
the electronic circuit of the IMU to improve the performance of IMU
and to adapt new inertial sensors.
[0217] Further, If the processing capability of the microprocessor
of the IMU is enough big, the operation tasks of the centralized
navigation Kalman filter 803 may be assigned to the micro-processor
of the IMU.
[0218] The architecture of the connection among the GPS digital
processing unit 802, the centralized navigation Kalman filter 803,
the interfaces 804, the IMU data sampling circuit 806 and the time
synchronizer 805 may have the following types:
[0219] (1) Bus-based connection structure
[0220] (2) Communication port-based connection structure
[0221] (3) Network-based connection structure.
* * * * *