U.S. patent application number 10/183975 was filed with the patent office on 2003-03-06 for motion tracking system.
This patent application is currently assigned to Massachusetts Institute of Technology, a Massachusetts Corporation. Invention is credited to Foxlin, Eric M..
Application Number | 20030045816 10/183975 |
Document ID | / |
Family ID | 22042517 |
Filed Date | 2003-03-06 |
United States Patent
Application |
20030045816 |
Kind Code |
A1 |
Foxlin, Eric M. |
March 6, 2003 |
Motion tracking system
Abstract
Tracking a motion of a body by obtaining two types of
measurements associated with the motion of the body, one of the
types including acoustic measurement. An estimate of either an
orientation or a position of the body is updated based on one of
the two types of measurement, for example based on inertial
measurement. The estimate is then updated based on the other of the
two types of measurements, for example based on acoustic ranging.
The invention also features determining range measurement to
selected reference devices that are fixed in the environment of the
body.
Inventors: |
Foxlin, Eric M.; (Arlington,
MA) |
Correspondence
Address: |
FISH & RICHARDSON PC
225 FRANKLIN ST
BOSTON
MA
02110
US
|
Assignee: |
Massachusetts Institute of
Technology, a Massachusetts Corporation
|
Family ID: |
22042517 |
Appl. No.: |
10/183975 |
Filed: |
June 25, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10183975 |
Jun 25, 2002 |
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09609424 |
Jul 5, 2000 |
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6409687 |
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09609424 |
Jul 5, 2000 |
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09062442 |
Apr 17, 1998 |
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6176837 |
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Current U.S.
Class: |
600/595 |
Current CPC
Class: |
G01S 5/186 20130101;
G01S 15/86 20200101; G01C 21/165 20130101; A61B 5/1038 20130101;
G01S 11/16 20130101; G01S 15/87 20130101 |
Class at
Publication: |
600/595 |
International
Class: |
A61B 005/103 |
Claims
What is claimed is:
1. A method for tracking a motion of a body comprising: obtaining
two types of measurements associated with the motion of the body,
one of the types comprising acoustic measurement; updating an
estimate of either an orientation or a position of the body based
on one of the two types of measurement; and updating the estimate
based on the other of the two types of measurements.
2. The method of claim 1 in which one of the types of measurement
comprises acoustic ranging.
3. The method of claim 1 in which the other of the types of
measurement comprises inertial measurement.
4. The method of claim 1 in which the estimate is of
orientation.
5. An apparatus for tracking motion of a body comprising: two
sensor systems configured respectively to obtain two types of
measurements associated with motion of the body, one of the types
comprising acoustic measurement; and a processor coupled to the two
sensor systems and configured to update an estimate of wither an
orientation or a position of the body based on one of the two types
of measurement, and to update the estimate based on the other of
the two types of measurement.
6. A tracking device comprising: a sensor system including an
inertial sensor; and a set of one or more acoustic sensors rigidly
coupled to the inertial sensor; and a processor programmed to
perform the functions of accepting inertial measurements from the
inertial sensor; updating a location estimate and an orientation
estimate of the sensor system using the accepted inertial
measurements; selecting one of a plurality of acoustic reference
devices; accepting an acoustic range measurement related to the
distance between the sensor system and the selected acoustic
reference device; updating the location estimate and the
orientation estimate using the accepted range measurement.
7. The tracking device of claim 6 wherein the sensor system
includes a transmitter for transmitting a control signal encoding
an identifier of the selected acoustic reference device, and each
acoustic sensor includes a microphone for receiving an acoustic
signal from the acoustic reference device.
8. The tracking device of claim 6 wherein the set of one or more
acoustic sensors includes two or more acoustic sensors.
9. The tracking device of claim 6 wherein updating a location
estimate and an orientation estimate using the accepted inertial
measurements includes updating an uncertainty in the location and
the orientation estimates; and updating the location estimate and
the orientation estimate using the accepted range measurement
includes determining an uncertainty in the range measurement, and
updating the uncertainty in the location and the orientation
estimates using the uncertainty in the range measurement.
10. A method for tracking the motion of a body including: selecting
one of a plurality of reference devices; transmitting a control
signal to the selected reference device; receiving an range
measurement signal from the reference device; accepting a range
measurement related to a distance to the selected reference device;
and updating a location estimate or an orientation estimate of the
body using the accepted range measurement.
11. The method of claim 10 further comprising: determining a range
measurement based on a time of flight of the range measurement
signal.
12. The method of claim 10 wherein transmitting the control signal
includes transmitting a wireless control signal.
13. Software stored on a computer readable medium comprising
instructions for causing a computer to perform the functions of:
selecting one of a plurality of reference devices; transmitting a
control signal to the selected reference device; receiving an range
measurement signal from the reference device; accepting a range
measurement related to a distance to the selected reference device;
and updating a location estimate or an orientation estimate of the
body using the accepted range measurement.
14. A tracking system comprising: an acoustic reference system
including a plurality of acoustic reference devices; and a tracking
device including a sensor system including an inertial sensor and a
set of one or more acoustic sensors rigidly coupled to the inertial
sensor, and a processor programmed to perform the functions of
accepting inertial measurements from the inertial sensor, updating
a location estimate and an orientation estimate of the sensor
system using the accepted inertial measurements, selecting one of a
plurality of acoustic reference devices, accepting an acoustic
range measurement related to the distance between the sensor system
and the selected acoustic reference device, and updating the
location estimate and the orientation estimate using the accepted
range measurement.
15. The system of claim 14 wherein the sensor system includes a
transmitter for transmitting a control signal encoding an
identifier of the selected acoustic reference device, and each
acoustic sensor includes a microphone for receiving an acoustic
signal from the acoustic reference device, and wherein each
acoustic reference device includes a receiver for receiving the
control signal from the sensor system, and an acoustic transducer
for sending the acoustic signal.
Description
BACKGROUND
[0001] The invention relates to motion tracking.
[0002] Motion tracking can use a variety of measurement modes,
including inertial and acoustic measurement modes, to determine the
location and orientation of a body.
[0003] Inertial motion tracking is based on measuring linear
acceleration and angular velocity about a set of typically
orthogonal axes. In one approach, multiple spinning gyroscopes
generate forces proportional to the rates at which their spinning
axes rotate in response to rotation of a tracked body to which the
gyroscopes are attached. These forces are measured and used to
estimate angular velocity of the body. Micro-machined vibrating
elements and optical waveguide based devices may be used in place
of gyroscopes.
[0004] Accelerometers generate signals proportional to forces which
result from linear acceleration. In an inertial tracking system,
the angular velocity and acceleration signals are integrated to
determine linear velocity, linear displacement, and total angles of
rotation.
[0005] As the signals generated by gyroscopic devices are noisy,
the integration process results in accumulation of noise
components, which is generally known as "drift". Miniaturized and
low cost gyroscopic devices typically exhibit greater error. Drift
rates can be as high as several degrees per second for a body at
rest, and several degrees for every rotation of the body by 90
degrees. Errors in orientation estimates also affect location
estimation as the estimated orientation of the body is used to
transform acceleration measurements into the fixed reference frame
of the environment prior to their integration. Inaccuracy in this
transformation can result in gravity appearing as a bias a
resulting horizontal acceleration measurements.
[0006] One way to correct drift is to use additional sensors, such
as inclinometers and a compass to occasionally or continually
correct the drift of the integrated inertial measurements. For
instance, U.S. Pat. No. 5,645,077, issued to Eric M. Foxlin on Jul.
8, 1997, discloses such an approach. This patent in incorporated
herein by reference.
[0007] Another approach to motion tracking uses acoustic waves to
measure distance between one or more points on a body and fixed
reference points in the environment. In one arrangement, termed an
"outside-in" arrangement, a set of acoustic emitters at the fixed
points on the body emit pulses that are received by a set of
microphones at the fixed reference points in the environment. The
time of flight from an emitter to a microphone is proportional to
an estimate of the distance between the emitter and the microphone
(i.e., the range). The range estimates from the emitters to the
respective microphones are used to triangulate the location of the
emitters. The locations of multiple emitters on the body are
combined to estimate the orientation of the body.
[0008] Other measurement modes, such as optical tracking of light
sources on a body, can also be used to track motion of the
body.
SUMMARY
[0009] In one aspect, in general, the invention is a method for
tracking a motion of a body which includes obtaining two types of
measurements associated with the motion of the body, one of the
types comprising acoustic measurement, updating an estimate of
either an orientation or a position of the body based on one of the
two types of measurement, for example based on inertial
measurement, and updating the estimate based on the other of the
two types of measurements, for example based on acoustic
ranging.
[0010] In another aspect, in general, the invention is a method for
tracking the motion of a body including selecting one of a set of
reference devices, transmitting a control signal to the selected
reference device, for example by transmitting a wireless control
signal, receiving an range measurement signal from the reference
device, accepting a range measurement related to a distance to the
selected reference device, and updating a location estimate or an
orientation estimate of the body using the accepted range
measurement. The method can further include determining a range
measurement based on a time of flight of the range measurement
signal.
[0011] Advantages of the invention include providing a
6-degree-of-freedom tracking capability that can function over an
essentially unlimited space in which an expandable constellation of
ultrasonic beacons is installed. Inertial measurements provide
smooth and responsive sensing of motion while the ultrasonic
measurements provide ongoing correction of errors, such as those
caused by drift of the inertial tracking component of the system.
Small and inexpensive inertial sensors, which often exhibit
relatively large drift, can be used while still providing an
overall system without unbounded drift. Small, lightweight inertial
sensors are well suited for head mounted tracking for virtual or
augmented reality display systems. By correcting drift using
ultrasonic measurements, drift correction measurements which may be
sensitive to external factors such as magnetic field variations,
are not needed. The constellation of ultrasonic beacons can be
easily expanded as each beacon functions independently and there is
no need for wiring among the beacons. The tracking device only
relies on use of a small number of ultrasonic beacons at any time,
thereby allowing the space in which the tracking device operates to
have irregular regions, such as multiple rooms in a building.
[0012] Another advantage of the invention is that by using an
"inside-out" configuration, there is no latency in acoustic range
measurements due to motion of the body after an acoustic wave is
emitted.
[0013] Yet another advantage of the invention is that tracking
continues using inertial measurements even when acoustic
measurements cannot be made, for example, due to occlusion of the
beacons. Drift in the inertial tracking is then corrected once
acoustic measurements can once again be made.
[0014] In yet another advantage, the invention provides
line-of-sight redundancy whereby one or more paths between emitters
and sensors can be block while still allowing tracking of a
body.
[0015] Other features and advantages of the invention will be
apparent from the following description, and from the claims.
DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 shows a tracking device and a constellation of
acoustic beacons used for tracking the device;
[0017] FIG. 2 shows components of a tracking device processor;
[0018] FIG. 3 illustrates a combined inertial and acoustic tracking
approach;
[0019] FIG. 4 shows an inertial measurement unit (IMU);
[0020] FIG. 5 shows an ultrasonic range measurement unit (URM) and
an ultrasonic beacon;
[0021] FIG. 6 shows an input/output interface used in a tracking
device processor to interface with inertial and ultrasonic
measurement units;
[0022] FIG. 7a illustrates the navigation and body frames of
reference;
[0023] FIG. 7b illustrates mutual tracking devices;
[0024] FIG. 8 is a signal flow diagram of an inertial tracker;
[0025] FIG. 9 is a signal flow diagram of an ultrasonic range
measurement subsystem;
[0026] FIG. 10 is a signal flow diagram of a tracking device
including an inertial tracker and Kalman predictor and updater
elements;
[0027] FIG. 11 is a signal flow diagram of a Kalman predictor;
[0028] FIG. 12 is a signal flow diagram of a Kalman updater;
[0029] FIG. 13 is a flowchart of a tracking procedure;
[0030] FIG. 14a illustrates tracking of a second body relative to a
first tracked body;
[0031] FIG. 14b illustrates mutual tracking of multiple
devices;
[0032] FIG. 15 illustrates head mounted display system;
[0033] FIG. 16 illustrates a camera tracking system for television;
and
[0034] FIG. 17 illustrates tracking of bodies in an automobile.
DESCRIPTION
[0035] Referring to FIG. 1, a tracking device 100 which maintains
an estimate of its location and orientation is free to move within
a large room. For example, tracking device 100 can be fixed to a
head-up display (HUD) on an operator's head, and tracking device
100 moves through the room, and changes orientation, as the
operator moves and orients his head. Tracking device 100 includes a
processor 130 coupled to an inertial measurement unit (IMU) 140
which provides inertial measurements related to linear acceleration
and to rates of rotation. Processor 130 uses the inertial
measurements to determine motion of tracking device 100 as it moves
through the room.
[0036] Processor 130 is also coupled to an array of three
ultrasonic range measurement units (URM) 110 which are used to
receive acoustic signals sent from an ultrasonic beacon array 120,
a "constellation" of beacons. Ultrasonic beacon array 120 includes
independent ultrasonic beacons 122 in fixed locations in the
environment, for example, arranged on the ceiling of the large room
in a regular pattern such as on a grid with 2 foot spacing.
Processor 130 uses the signals from particular ultrasonic beacons
122, as well as known three-dimensional locations of those beacons,
to estimate the range to those beacons and thereby sense motion for
tracking device 100. Each ultrasonic beacon 122 sends an ultrasonic
pulse 114 in response to infra-red command signal 112 sent from
tracking device 100. In particular, each URM 110 on tracking device
100 broadcasts infra-red (IR) signals to all of the ultrasonic
beacons 122. These IR signals include address information so that
only one beacon, or a small number of beacons, recognize each IR
signal as intended for it, and responds to the signal. In response
to an IR signal, an addressed beacon immediately broadcasts an
ultrasonic pulse that is then received by one or more URM 110. As
processor 130 knows that the addressed beacon responded immediately
to the IR command, it determines the time of flight by measuring
the delay from issuing the IR command to detecting the ultrasonic
pulse. The time of flight of the ultrasonic pulse is used to
estimate the range to the beacon, which is then used to update the
position and orientation of tracking device 100.
[0037] Both the inertial measurements and the ultrasonic signal
based measurements have limitations. Relying on either mode of
measurement individually is not as accurate as combining the
measurements. Tracking device 100 combines measurements from both
measurement modes and adjusts its estimate of position and
orientation (i.e., 6 degrees of freedom, "6-DOF") to reflect
measurements from both modes as they are available, or after some
delay. To do this, processor 130 hosts an implementation of an
extended Kalman filter (EKF) that is used to combine the
measurements and maintain ongoing estimates of location and
orientation of tracking device 100, as well as to maintain an
estimate of the uncertainty in those estimates.
[0038] Referring to FIG. 2, processor 130 includes a central
processing unit (CPU) 200, such as an Intel 80486 microprocessor,
program storage 220, such as read-only memory (ROM), and working
storage 230, such as dynamic random-access memory (RAM). CPU 200 is
also coupled to an input/output interface 210 which provide an
interface to IMU 140 and the URM 110. Input/output interface 210
includes digital logic that provides digital interfaces to IMU 140
and the URM 110.
[0039] IMU 140 provides a serial data stream 201 encoding inertial
measurements. Input/output interface 210 converts this serial data
to a parallel form 212 for transfer to CPU 200. Each URM 110
accepts a serial signal 211 that is used to drive an IR light
emitting diode 510 to broadcast the IR control signals to
ultrasonic beacons 122 (FIG. 1).
[0040] Input/output interface 210 accepts address information from
CPU 200 identifying one or more ultrasonic beacons and provides the
serial signal to each of the URM 110 which then impose the serial
signal on an IR transmission (e.g., by amplitude modulation). The
same serial signal is provided to all the URMs 110, which
concurrently broadcast the same IR signal. Each URM 110 provides in
return a logical signal 202 to input/output interface 210
indicating arrivals of ultrasonic pulses. Input/output interface
210 includes timers that determine the time of flight of ultrasonic
pulses from the beacons, and thereby determines range estimates to
the beacons. These range estimates are provided to CPU 200.
[0041] An implementation of a tracking algorithm is stored in
program storage 220 and executed by CPU 200 to convert the
measurements obtained from input/output interface 210 into position
and orientation estimates. CPU 200 is also coupled to fixed data
storage 240, which includes information such as a predetermined map
of the locations of the ultrasonic beacons, and the locations of
the microphones of the URM 110. Processor 130 also includes a
communication interface 260 for coupling CPU 200 with other
devices, such as a display device 280 that modifies its display
based on the position and orientation of tracking device 100.
[0042] Operation of the system can be understood by referring to
FIG. 3, a two-dimensional view of the room shown in FIG. 1 (from
above). The sequence of open circles and arrows 310a-e represent
the actual location and orientation of tracking device 100 at each
of a sequence of time steps. Based on prior measurements, and on
inertial measurements at the first time step, filled circle and
arrow 312a represent the estimate by tracking device 100 of the
location and orientation of the tracking device at the first time
step. At the next time step, tracking device 100 moves to position
310b, and based on a new inertial measurement, tracking device 100
updates its position estimate to 312b. This is repeated for the
next time step with actual position 310c and estimated position
312c.
[0043] After reaching position 310b, tracking device 100 sends an
IR command addressed one of the ultrasonic transducers 122,
illustrated by dotted line 320. After receiving the IR command
(with essentially no delay), ultrasonic transducer 122 transmits an
ultrasonic pulse, illustrated by wave 324. Wave 324 reaches
tracking device 100 some time later, at actual location 330. Based
on the time of arrival, tracking device 100 estimates that it was
at position 332 when wave 326 reached it.
[0044] At the next time step, tracking device 100 first estimates
its position 312d based on an inertial measurement. Using range
information related to the separation of the location of ultrasonic
transducer 122 and location 332 and a measured time of flight of
the ultrasonic wave, tracking device 100 computes a refined
position estimate 312d'. The process repeats using inertial
measurements at true position 310e and estimated position 312e.
[0045] In general, both an inertial measurement and an ultrasonic
measurement can be used at each time step, although ultrasonic
measurement can be made less frequently. At each time step, both
location and orientation (attitude) is updated. The ultrasonic
pulses can provide information related to both location and
orientation through the use of multiple microphones that are
displaced relative to one another.
[0046] Referring to FIG. 4, inertial measurement unit (IMU) 140
includes three angular rate sensors (e.g., micro-machined vibrating
rotation sensors or small rotating gyroscopes) 420a-c, and three
linear acceleration sensors 410a-c. The sensors are arranged to lie
along three orthogonal axes that remain fixed in the frame of
reference of tracking device 100. Each acceleration sensor provides
a signal that is generally proportional to the acceleration along
the corresponding axis, and each angular rate sensor provides a
signal that is generally proportional to the rate of rotation about
the corresponding axis.
[0047] As the orientation of inertial measurement unit 140 changes,
the signals such as the acceleration signals correspond to changing
directions in the fixed (navigation) reference frame of the room.
Inertial measurement unit 140 also includes a signal interface 430
which accepts the signals 411 from each of the six accelerometers
and angular rate sensors, and transmits a serial data stream 413
which multiplexes digital representations of the acceleration and
angular rate signals. As is discussed further below, the
acceleration and angular rate signals are imperfect, and may
exhibit additive bias and scaling inaccuracies. These scaling and
bias inaccuracies may depend on the motion of the device.
[0048] Referring to FIG. 5, each ultrasonic measurement unit 110
includes an infra-red (IR) light-emitting diode (LED) 510 that is
driven by IR signal generator 512. Signal generator 512 accepts
serial signal 211 from input/output interface 210 (FIG. 2) and
drives IR LED 510 to transmit that signal to one or more ultrasonic
beacon 122. The address of an ultrasonic beacon to which a range is
desired is encoded in serial signal 211. Each ultrasonic beacon 122
includes an IR sensor 540 which, if there is a sufficiently short
unobstructed path between ultrasonic range measurement unit 110 and
that ultrasonic beacon, receives the IR signal which is then
decoded by IR signal decoder 542. This decoded signal includes the
address information transmitted by the ultrasonic range measurement
unit. Control circuitry 560 receives the decoded IR signal, and
determines whether that ultrasonic beacon is indeed being
addressed, and if so, signals a pulse generator 552 to provide a
signal to an ultrasonic transducer 550 which generates an
ultrasonic pulse. The pulse passes through the air to ultrasonic
range measurement unit 110 where a microphone 520 receives the
ultrasonic pulse and passes a corresponding electrical signal to a
pulse detector 522 which produces a logical signal indicating
arrival of the pulse. This pulse detection signal is passed to
input/output interface 210 (FIG. 2). As discussed below, the time
of flight is not a perfectly accurate measurement of range. Error
sources include timing errors in detection of the pulse, acoustic
propagation rate variations, for example due to air temperature or
air flow, and non-uniform in different directions propagation of
the ultrasonic wave from the ultrasonic beacon.
[0049] Input/output interface 210 includes circuitry (i.e., a
programmable logic array) which implements logical components shown
in FIG. 6. An IMU data buffer 630 accepts serially encoded
acceleration and angular rate data 413 from IMU 140, and provides
the six acceleration and rotation measurements 631 as output to CPU
200. Input/output interface 210 also includes a beacon address
buffer 610. CPU 200 (FIG. 2) provides an address of the ultrasonic
beacon to which a range should be measured. Beacon address buffer
610 stores the address and provides that address in serial form to
each of the URMs 110. At the same time that the address is
transmitted by each of the URM 110 (and received by the ultrasonic
beacons 122), three counters 620a-c are reset and begin
incrementing from zero at a fixed clocking rate (e.g., 2 MHz). When
each URM 110 detects the ultrasonic pulse from the beacon, the
corresponding pulse detection signal is passed to the corresponding
counter which stops counting. The counts are then available to CPU
200 as the measurements of the time of flight of the ultrasonic
pulse from the ultrasonic beacon to each URM 110.
[0050] Referring to FIGS. 7a-b, tracking device 100 (FIG. 1)
determines its location in the navigation reference frame of the
room, shown as axes 710, labeled N (north), E (east), and D (down).
Location r.sup.(n) 730 is a vector with components
(r.sub.N.sup.(n), r.sub.E.sup.(n), r.sub.D.sup.(n)).sup.T of the
displacement from axes 710 in the N, E, and D directions
respectively. Tracking device 100 also determines its attitude
(orientation).
[0051] Referring to FIG. 7b, attitude is represented in terms of
the roll, pitch, and yaw (Euler) angles, .theta.=(.psi., .theta.,
.phi.).sup.T, needed to align the body attitude, represented by
coordinate axes 720, with the navigation attitude represented by
coordinate axes 710. The three Euler angles are represented as a
3.times.3 direction cosine matrix, C.sub.b.sup.n(.theta.), which
transforms a vector of coordinates in the body frame of reference
by essentially applying in sequence yaw, pitch, and then roll
motions around the z, y, and then x axes. The direction cosine
matrix can be defined as 1 C ( _ ) = [ 1 0 0 0 cos - sin 0 sin cos
] [ cos 0 - sin 0 1 0 sin 0 cos ] [ cos - sin 0 sin cos 0 0 0 1
]
[0052] The superscript and subscript notation C.sub.b.sup.n
signifies that the matrix takes a vector in the "b" (body)
reference frame and provides a vector in the "n" (navigation)
reference frame.
[0053] Referring to FIG. 8, inertial sensors 800, including
rotation sensors 420a-c and acceleration sensors 410a-c, provide
inertial measurement signals to an inertial tracker 810. Inertial
tracker 810 implements a discrete time approximation of the signal
flow shown in the FIG. 8. Inertial tracker 810 includes several
stages. First, gyroscope compensation 820 modifies the (vector)
angular rate signal .omega. to account for bias in the measurement.
In this example, only an additive bias .delta..omega. is corrected.
Other biases such as a multiplicative error (e.g., an incorrect
scale factor), and errors due to mounting inaccuracies can be
corrected as well. Accelerometer compensation 830 similarly
corrects for an additive bias .delta.a.sup.(b) on the acceleration
signals a.sup.(b). As is discussed fully below, several parameters,
including the bias terms .delta..omega. and .delta.a.sup.(b), are
estimated using ultrasonic measurements.
[0054] Attitude integration 840 updates the attitude estimate based
on the bias corrected rotation signal. In this example, attitude
integration is performed using a direction cosine representation of
the attitude. A discrete time implementation of the continuous
differential equation {dot over
(C)}.sub.b.sup.n(t)=C.sub.b.sup.n(t) S(.omega.(t)) is used to
update the direction cosine matrix at a fixed rate, typically
between 100 and 200 per second. Changing notation to a discrete
time system (e.g., C.sub.k=C.sub.b.sup.n(k.DELTA.t)), the discrete
time update of the direction cosine matrix is implemented as 2 C k
= C k - 1 ( I + sin S ( _ ) + 1 - cos 2 S ( _ ) 2 ) where _ = _ k -
1 + _ k 2 t , = ; _ r; and S ( _ ) = [ 0 - z y z 0 - x - y x 0
]
[0055] is the skew symmetric matrix of .delta..theta.. Note that
S(.delta..THETA.) satisfies
S(.delta..THETA.).sup.2=.delta..theta..sup.2I-.delta..theta..delta..theta.-
.sup.T.
[0056] In order to ensure that C.sub.k truly is a direction cosine
matrix, its rows are orthonormalized after each iteration to remove
any numerical or approximation errors that may have entered into
its entries.
[0057] Based on the tracked direction cosine matrix C.sub.k,
coordinate transformation 850 accepts the bias corrected
acceleration signal in the body reference frame and outputs an
acceleration signal in the navigation reference frame according
to
a.sub.k.sup.(n)=C.sub.k({tilde over
(a)}.sub.k.sup.(b)-.delta.a.sub.k.sup.- (b))+(0,0,-g).sup.T.
[0058] Double integration 850 then computes the velocity and
position according to 3 v _ k ( n ) = v _ k - 1 ( n ) + a _ k - 1 (
n ) + a _ k ( n ) 2 t , and r _ k ( n ) = r _ k - 1 ( n ) + v _ k -
1 ( n ) t + 2 a _ k - 1 ( n ) + a _ k ( n ) 6 t 2 .
[0059] Euler angle computation 870 takes the direction cosine
matrix and outputs the corresponding Euler angles. The output of
inertial tracker 810 is (.theta., r.sup.(n)).sup.T. The state of
the inertial tracker includes a 15-dimensional vector composed on
five sets of three-dimensional values
x=(.theta., .omega., r.sup.(n), y.sup.(n), a.sup.(n)).sup.T.
[0060] As is discussed fully below, inertial tracker 810 receives
error update signals .delta.x derived from ultrasonic range
measurements that it uses to correct the attitude, velocity, and
position values, and to update the parameters of the gyroscope and
accelerometer bias correction elements.
[0061] Referring to FIG. 9, a beacon sequencer 910 receives
location estimates r.sup.(n) from inertial tracker 810. Using a
beacon map 915 of the locations (and addresses) of the ultrasonic
beacons 122 (shown in FIG. 1), beacon sequencer 910 determines
which beacon to trigger at each time step in order to generate
ultrasonic range measurements. For instance, beacon sequencer 910
determines the closest beacons to the current location, and cycles
among these beacons on each time step. As the location estimate
changes, the set of closest beacons also, in general, changes.
After beacon sequencer 910 triggers each of the beacons in turn,
the corresponding ultrasonic pulses arrive and are detected by the
tracking device. Each pulse generates one range measurement for
each microphone used to detect the pulse. In this embodiment, each
pulse generates a set of three range measurements, one from each of
the microphones in the three URM 110.
[0062] Referring still to FIG. 9, range measurement 920 corresponds
to the process of receiving an ultrasonic range estimate. The
relevant parameters for a range measurement are the location of the
addressed beacon, b.sup.(n), the location of the microphone used to
detect the pulse, m.sup.(b), the range estimate itself, d.sub.r,
and the time the pulse was detected, t.sub.r, which is used to
correct for latency in the measurements. Note that if the location
estimate had no error, and the range estimate was perfectly
accurate, then the range estimate would satisfy
d.sub.r=.parallel.b.sup.(n)-(r.sup.(n)(t.sub.r)+C.sub.b.sup.n(t.sub.r)m.su-
p.(b)).parallel..
[0063] Deviations from this equality are used to correct the
parameters and outputs of inertial tracker 810.
[0064] A complementary Kalman filter is used by tracking device 100
to improve the tracked location and orientation estimate by
incrementally updating the tracked quantities as the range
measurements come in. Referring to FIG. 10, the approach involves
two related components. As inertial tracker 810 updates its output
x, a Kalman predictor 1010 maintains an estimated covariance matrix
P of the error in x. For instance, in the absence of any drift
compensation in inertial tracker 810, the covariance matrix P would
correspond to an ever increasing error.
[0065] The second component used in this approach is a Kalman
updater 1020 which accepts information from range measurement 920
and using this measurement information determines an estimate of
the accumulated error .delta.x which it feeds back to inertial
tracker 810 where it is used to update x. Also, after each
ultrasonic measurement, Kalman updater 1020 computes a new
estimated covariance matrix P(+) of the error in x after the
update, which it feeds back to Kalman predictor 1010. Each
ultrasonic measurement partially corrects the output of inertial
tracker 810. A continuous series of ultrasonic updates ensures that
the error remains small.
[0066] Inertial tracker 810 is a nonlinear processor of its inputs,
and therefore, a formulation of a Kalman filter for a purely linear
filter driven by Gaussian noise is not appropriate. Using what is
generally known as an "extended Kalman filter" (EKF), a linearized
dynamical system model which characterizes the propagation of error
in the output x of inertial tracker 810 is used. The error that the
EKF models is
.delta.x=(.phi., .delta..omega..sup.(b), .delta.r.sup.(n),
.delta.v.sup.(n), .delta.a.sup.(b)).sup.T
[0067] with the components corresponding to the components of the
vector output of the inertial tracker. Note that the error term
.delta.a.sup.(b) is modeled in the body coordinate system rather
than in the navigation coordinate system, and that the other
elements correspond directly to errors in the output of inertial
tracker 810. The parameters of the linearized error propagation
model include a state transition matrix, and a covariance matrix of
a driving noise which is assumed to drive this error model. Both
the state transition matrix and the driving noise covariance depend
on the output of inertial tracker. In the absence of any
measurements, the mean of the error process remains zero. However,
the covariance of the error grows. The linearized model of error
propagation is
.delta.x.sub.k=F(x.sub.k-1).delta.x.sub.k-1+w.sub.k-1.
[0068] The entries of F.sub.k=F(x.sub.k-1) are derived from a
perturbation analysis of the update equations used in inertial
tracker 810, and correspond to the following error propagation
equations: 4 _ k = _ k - 1 - C b n _ k - 1 , _ k = _ k - 1 , r _ k
= r _ k - 1 + t v _ k - 1 - 1 2 t 2 ( C b n ( a _ ) k - 1 ( b ) - S
( _ k - 1 ) ( a _ k - 1 ( n ) + ( 0 , 0 , - g ) T ) ) v _ k = v _ k
- 1 + t a _ k - 1 ( b ) - t S ( _ k - 1 ) ( a _ k - 1 ( n ) + ( 0 ,
0 , - g ) T ) , and a _ k ( b ) = a _ k - 1 ( b ) .
[0069] The covariance Q.sub.k of the process noise w.sub.k is
assumed to be diagonal. The entries of this covariance matrix are
derived from known sources of error in the inertial measurements
provided to inertial tracker 810, including additive bias errors,
scaling errors, alignment errors of the sensors with the body axes,
and signal noise from the sensors themselves. The individual
variances depend on the output of the inertial tracker as
follows:
Q.sub.k=diag(.sigma..sub..phi..sub..sub.x.sup.2,.sigma..sub..phi..sub..sub-
.y.sup.2,.sigma..sub..phi..sub..sub.z.sup.2,.sigma..sub..omega..sup.2,.sig-
ma..sub..omega..sup.2,.sigma..sub..omega..sup.2,.sigma..sub.r.sub..sub.x.s-
up.2,.sigma..sub.r.sub..sub.y.sup.2,.sigma..sub.r.sub..sub.z.sup.2,.sigma.-
.sub.v.sub..sub.x.sup.2,.sigma..sub.v.sub..sub.y.sup.2,.sigma..sub.v.sub..-
sub.z.sup.2,.sigma..sub.a.sup.2,.sigma..sub.a.sup.2,.sigma..sub.a.sup.2)
[0070] where the individual variance terms are parameterized as
follows:
.sigma..sub..phi..sub..sub.x=GyroScale
.omega..sub.x.DELTA.t+GyroAlign
(.omega..sub.y+.omega..sub.z).DELTA.t+GyroNoise {square
root}{square root over (.DELTA.t)}
.sigma..sub..phi..sub..sub.y=GyroScale
.omega..sub.y.DELTA.t+GyroAlign
(.omega..sub.x+.omega..sub.z).DELTA.t+GyroNoise {square
root}{square root over (.DELTA.t)}
.sigma..sub..phi..sub..sub.z=GyroScale
.omega..sub.z.DELTA.t+GyroAlign
(.omega..sub.x+.omega..sub.y).DELTA.t+GyroNoise {square
root}{square root over (.DELTA.t)}
.sigma..sub..omega.=(GyroBiasChangeRate .DELTA.t
.sigma..sub.r.sub..sub.x=.sigma..sub.r.sub..sub.y=.sigma..sub.r.sub..sub.z-
=0
.sigma..sub.v.sub..sub.x=AccelScale a.sub.x.DELTA.t+AccelAlign
(a.sub.y+a.sub.z).DELTA.t+AccelNoise {square root}{square root over
(.DELTA.t)}
.sigma..sub.v.sub..sub.y=AccelScale a.sub.y.DELTA.t+AccelAlign
(a.sub.x+a.sub.z).DELTA.t+AccelNoise {square root}{square root over
(.DELTA.t)}
.sigma..sub.v.sub..sub.z=AccelScale a.sub.z.DELTA.t+AccelAlign
(a.sub.x+a.sub.y).DELTA.t+AccelNoise {square root}{square root over
(.DELTA.t)}
.sigma..sub.a.sup.2=AccelBiasChangeRate .DELTA.t
[0071] where GyroScale, AccelScale, GyroAlign, and AccelAlign
correspond to degree of uncertainty in calibration coefficients
used for instrument error compensation. In general, a non-diagonal
process noise covariance can be used.
[0072] Referring to FIG. 11, Kalman predictor 1010 has two stages.
An error linearization stage 1110 first computes F.sub.k and
Q.sub.k as outlined above. Then, a covariance propagation stage
1120 iteratively updates the error covariance by applying a Kalman
filter covariance propagation equation
P.sub.k=F.sub.k-1P.sub.k-1F.sub.k-1.sup.T+Q.sub.k
[0073] on each time step. When Kalman predictor 1010 receives an
updated covariance P(+), which is produced as a result of an
ultrasonic range measurement, that updated covariance replaces the
current error covariance P.
[0074] Referring to FIG. 12, Kalman updater 1020 accepts the output
of range measurement 920, as well as the estimate of location and
orientation from inertial tracker 810, and the covariance of the
error of the estimate of location and orientation from Kalman
predictor 1010, and computes an error estimate, and an updated
covariance that results from applying the error estimate. A first
stage of Kalman updater 1020 is measurement residual computation
1210. The difference between the expected range and the measured
range is computed as
.delta.d.sub.r=d.sub.r-.parallel.b.sup.(n)-(r.sup.(n)(t.sub.r)+C.sub.b.sup-
.n(t.sub.r){square root over (m)}.sup.(b).parallel..
[0075] Note that in general a range measurement is used some time
after it was initially detected. In order to account for this
latency, estimates of the location and orientation of the tracking
device at the time that the acoustic pulse arrived are used rather
than the location and orientation at the time that the measurement
is used. The current location, orientation, and linear and angular
rate estimates are used to extrapolate back to the measurement time
to determine r.sup.(n)(t.sub.r) and C.sub.b.sup.n(t.sub.r).
[0076] In order to apply the Kalman update equations, this residual
is modeled using a linearized observation equation as
.delta.d.sub.r=H(x,b,d.sub.r,m).delta.x+v.
[0077] The observation matrix H.sub.k=H(x.sub.k,b,d.sub.r,m) is the
linear effect of errors in location and orientation on the error in
range measurement. The additive noise v has a variance
R(x.sub.k,b,d.sub.r,m). H.sub.k has the form 5 H k = ( b D m E - b
E m D + r E m D + r E m N - r D m E d r , b N m D - b D m N + r D m
N - r N m D d r , b E m N - b N m E + r N m E - r E m N d r , 0 , 0
, 0 , r N + m N - b N d r , r E + m E - b E d r , r D + m D - b D d
r , 0 , 0 , 0 , 0 , 0 , 0 )
[0078] The variance R(x.sub.k,b,d.sub.r,m) is derived to model
various phenomena associated with ultrasonic range measurement. For
example, as the range increases, pulse detection is more difficult,
due in part to pulse spreading, and an increased variance is used
to model the associated range measurement error. The variance
R(x.sub.k,b,d.sub.r,m) has the form
R=.sigma..sub.u.sup.2+.sigma..sub.f.sup.2
[0079] and is parameterized as
.sigma..sub.u.sup.2=NoiseFloor+NoiseScale d.sub.r
[0080] and
.sigma..sub.f.sup.2=(k.DELTA.t-t.sub.r)H.sub.k(.omega..sub.x,.omega..sub.y-
,.omega..sub.z,0,0,0,v.sub.x,v.sub.y,v.sub.z,0,0,0,0,0,0).sup.T
[0081] The first two terms of H.sub.k can alternatively be set to
zero to allow accelerometric tilt correction (if it is more
accurate). It the third term is set to zero, yaw drift correction
will occur over a longer time period but to higher accuracy.
[0082] Kalman updater 1020 includes a measurement accept/reject
stage 1230. Accept/reject stage 1230 takes the measurement
residual, .delta.x, and the computed variance, R, of the
measurement residual. If the measurement residual is greater in
magnitude than a predetermined multiple of the computed standard
deviation of the measurement residual, then the measurement is
rejected as being suspect, for example, due to premature or late
triggering of an ultrasonic pulse detector. Otherwise the
measurement residual is further processed to compute the state
error estimate, .delta.x. Using Kalman filter update equations,
Kalman gain computation 1240 computes the Kalman gain as
K=P.sub.kH.sub.k.sup.T(H.sub.kP.sub.kH.sub.k.sup.T+R).sup.-1.
[0083] Error estimator 1250 then computes the error estimate as
.delta.x=K .delta.d , and covariance updater 1260 computes the
updated error covariance as
P(+)=(I-KH)P.sub.k.
[0084] The components of .delta.x are then used to update inertial
tracker 810. The computed terms .delta..omega. and .delta.a.sup.(b)
are passed to gyroscope bias correction 820 and accelerometer bias
correction 830 (FIG. 8), respectively, where they are added to the
current stored bias parameters. The computed terms .delta.v.sup.(n)
and .delta.r.sup.(n) are passed to double integration 860 (FIG. 8)
where they are added to the current estimates of v.sup.(n) and
r.sup.(n), respectively. Finally, the direction cosine matrix is
updated according to
C.sub.k.rarw.(I-S(.phi.))C.sub.k
[0085] and re-orthonormalized.
[0086] Referring back to FIG. 1, ultrasonic beacon array 120
includes individual ultrasonic beacons 122 arranged in a regular
pattern. For example, the beacons may be arranged on a square grid
with a spacing of approximately 2 feet, preferably with an accuracy
of 3 mm or less. A limited number of addresses are available for
the beacons, in this embodiment only eight different addresses are
available due to hardware limitations. Therefore, when the tracking
device sends an IR command to an address, in general, multiple
ultrasonic beacons will receive the signal and respond. Only the
closest beacon with any particular address is used for range
measurement. However, as multiple beacons may be responding to each
IR command, the pulse detection circuit may be triggered
prematurely, for example, by a pulse from a beacon triggered in a
previous iteration, but that is sufficiently far away that its
pulse does not arrive until after a subsequent iteration. In order
to avoid this pre-triggering problem, pulse detector 522 (FIG. 5)
is only enabled during a time window about the expect time the
desired pulse would arrive. This avoids false triggering by pulses
from other beacons, or signals resulting from long time constant
reverberation of previous pulses.
[0087] In the description the tracking and Kalman updating
procedures, an initial location and orientation estimate is assumed
to be known. This is not necessarily the case and an automatic
acquisition algorithm is used by tracking device 100. The limited
number of addresses of ultrasonic beacons is used as the basis for
an initial acquisition algorithm. Initially, the tracking device
triggers beacons with each of the allowable addresses and measures
the range to the closest beacon of each address. Then, the
addresses of the four closest beacons are determined from the range
measurements. The tracking unit includes a beacon map that includes
the locations and addresses of all the beacons. The beacons are
arranged such that the addresses of the four closest beacons limit
the possible locations to a small portion of the room. If there is
ambiguity based on the closest beacons, the actual distances to the
beacons are used in a triangulation procedure to resolve the
ambiguity. The initial orientation is based on the relative range
measurements to each of the microphones.
[0088] The overall tracking procedure can be summarized by the
flowchart shown in FIG. 13. First, the initial location and
orientation is acquired (step 1310) using the approach outlined
above. The procedure then enters a loop that is executed once each
time step. After waiting for the next time step (step 1320),
inertial measurements are received (step 1330) and the tracked
variables, x, and the error covariance, P, are updated using the
inertial measurements (step 1340). If an ultrasonic range
measurement that has not yet been processed is available (step
1350), that range measurement is used to compute an error update,
.delta.x, and updated error covariance, P(+), (step 1360). The
error update and new error covariance are then used to update the
inertial tracker and the Kalman predictor (step 1370). The
procedure then involves determining whether further range
measurements must be commanded at this time step (step 1380). As
three range measurements are made for each pulse but only one range
measurement is used per time step, there may be a backlog of range
measurements that will be applied in the upcoming time steps.
Therefore, a new range measurement may not be necessarily for
several future time steps. Taking into account the expected time of
flight of the next ultrasonic pulse (which in general is more than
a single time step), the procedure determines if an IR command
should be sent to a beacon at this time step (step 1380), the next
beacon address is selected (step 1390) and, if so, the IR command
to that beacon is sent (step 1395). The procedure then loops again
starting at step 1320, waiting for the start of the next time
interval.
[0089] Several alternative approaches can also be used. In the
described embodiment, only one range measurement is used per time
step. Alternatively, all available range measurements can be used
at each time step if the processor 130 has sufficient computation
capacity. This alternative approach is implemented by looping from
step 1370 back to step 1350 until all the range measurements are
accounted for. Alternatively, rather than applying the Kalman
updates for each of the scalar range measurements in turn, all can
be applied in a single step using similar update equations for
vector observations and correlated observation noise. Also, rather
than deferring processing of a range measurement until the next
time step, the range measurements can be incorporated as they
arrive, and not synchronized with the inertial tracker updates.
[0090] The procedure described above can be combined with other
measurement modes. For example, inclinometers can be used to
provide measurements to the extended Kalman filter that allow
correction of attitude drift. Also, rather than using three or more
microphones which allow correction of all three degrees of
rotation, two microphones can be used for range measurement in
combination with a measurement mode such as inclinometers. In this
way, some drift correction can be based on inclinometers, but a
compass, which is sensitive to magnetic field variations, is not
needed for drift correction. Many more than three microphones can
also be used to provide greater redundancy and allow more rotation
freedom.
[0091] As an alternative to mounting beacons in fixed locations in
the environment, and microphones on the tracking device, which is
often referred to as an "inside-out" arrangement, this could be
reversed in an "outside-in" arrangement. The tracking device then
provides the ultrasonic pulses and a coordinated array of
microphones senses the location of the tracking device. Note that
by the time a pulse has reached a microphone, the tracking device
will have, in general, moved on to a new location. This latency of
measurements must be compensated for in a manner similar to the
compensation of latency in use of range measurements described
above.
[0092] Beacons 122 need not be mounted in a planar array. They
could be mounted on walls as well as on the ceiling, or on other
supports in the environment. For example, the beacons can be
mounted on light fixtures. The number of beacons can be chosen to
match the user's requirements, and the locations of the beacons can
be chosen based on a variety of criterea, such as availability of
suitable mounting points and geometric considerations, and the
beacon map can be set to match the chosen number and locations of
the beacons. The number of beacons in the constellation can be
increase or reduced by the user, so long as the beacon map remains
up to date.
[0093] The command signals from the tracking device to the beacons
can be sent using other modes than IR transmission. For example,
RF, visible, or acoustic signals can be used. The tracking device
can also be wired to the beacons.
[0094] Two or more objects can be tracked in an "inside-outside-in"
arrangement. Referring to FIG. 14a, tracking device 100 tracks its
location as before. A second tracking device 1400 includes three
addressable ultrasonic beacons 1410 arranged in a known
relationship to one another. By triggering beacons 1410 to transmit
acoustic pulses that are received at the URM 110 on tracking device
100, tracking device can determine the relative location and
orientation of the second tracking device. A further extension,
which provides increased accuracy in the relative location and
orientation estimates involves having a second inertial measurement
unit fixed to tracking device 1400, and transmitting its inertial
measurements to tracking device 100. If only a single beacon is
placed on the second object, the relative location can be sensed
using ultrasonic range measurements, without necessarily tracking
the relative orientation of the second device.
[0095] Referring to FIG. 14b, a "mutual tracking network" made up
of multiple tracking devices can be used. These tracking devices
track their individual locations with respect to the locations of
the other devices in the environment, including fixed beacons and
other moving tracked objects. This can be done with an addition
communication system coupling the tracking devices, such as an RF
local area network.
[0096] In the above described embodiments, the "map" of the beacon
array is assumed to be accurate. As the range measurements include
redundant information, errors in placement of the beacons can be
iteratively estimated and updated, thereby improving accuracy.
Specifically, the placement errors of the beacons can be included
in the state of the extended Kalman filter, and range measurements
from each beacon would then contribute over time to estimating the
placement errors. A separate initial automatic "mapping" mode can
also be used in which, through range measurement from one or more
locations in the room and triangulation calculations, the locations
of the beacons can be determined. These automatically determined
locations can be used as the known locations, or as initial
estimates that are then further updated using the Kalman filter. In
this type of approach, the beacons can be irregularly placed within
the room without requiring that they be precisely positioned.
[0097] The tracking approach described above has several
applications. A first application involves coupling the tracking
device to a head mounted display. Referring to FIG. 15, a head
mounted display 1510, allows a user to directly view a physical
object 1520, such as a work piece. Display 1510, using the known
location of work piece 1520 in the frame of reference of the room,
superimposes information on the user's view of the work piece. For
example, applying wiring harnesses to a large device, the
superimposed information can include information related to the
correct placement of the wiring harnesses. A similar head mounted
display can also be used to provide the complete image viewed by a
user in a virtual reality system, rather than superimposing an
image on the real view seen by the user.
[0098] Another application involves tracking a camera location in a
television application. Referring to FIG. 16, a common technique in
television production is to film a subject 1620 in front of a blank
(typically monochrome) background and then to electronically
superimpose another image (illustrated as 1630) as a background. A
difficulty with such a technique is that as camera 1610 moves, the
background image should change to reflect the camera's motion. By
attaching tracking device 100 to camera 1610, the location and
orientation of the camera is tracked and the background image can
be automatically modified by an image processor that receives the
changing position and orientation of the camera. This approach
allows construction of large "virtual sets" which is stored in the
image processor, and thereby multiple and changing camera "angles"
can be used.
[0099] Another application involves sensing of motion of elements
in an automobile, for example, in an automotive crash test.
Referring to FIG. 17, the motion of a dummy 1720 within a crashing
automobile 1710 can be tracked using tracking device 100. In
addition, a second object, such as a point on the firewall can be
tracked using an addition beacon 1730 using the inside-outside-in
approach described above. This allows both tracking of the dummy in
the reference frame of the automobile, and tracking of a point
within the vehicle relative to the dummy.
[0100] Other applications include robotic navigation, tracking of
inventory, assets, or personnel, shipboard virtual or augmented
reality for damage control, film camera tracking, entertainment
(e.g., theme parks and games), full body tracking for motion
capture, and weapon tracking.
[0101] Alternative embodiments can also use other approaches to
inertial tracking. For example, rather than performing attitude
integration using a direction cosine matrix, attitude integration
using Euler angles or quaternions can equivalently be used. Note
that the linearized error propagation system matrix and driving
noise covariance may depend somewhat on the particular tracking
algorithm used. Also, the state of the Kalman filter can be
changed, for instance, to include other terms. One example of this
is to not only track accelerometer additive bias, as in the
embodiments described above, but also to track multiplicative bias
(e.g., error in scale factor) of the accelerometer signal,
misalignment, and the speed of sound.
[0102] Other methods of range measurement can also be used,
including acoustic phase, RF or optical time of flight, RF or
optical phase, and mechanical cable extension.
[0103] Other methods of fusing inertial and acoustic measurements
can be used instead of Kalman filtering. For example, neural
network, rule-based reasoning, or fuzzy logic systems, or
optimization methods, can be used to combine the measurements.
[0104] In the description above, only eight different ultrasonic
beacon addresses are used. Alternatively, each beacon can be
individually addressable, or a larger number of shared addresses
can be used. If the beacons are individually addressable, initial
acquisition can be performed, for example, by having beacons also
respond to "group" addresses, or to sequence commands addressed to
individual beacons during the acquisition phase in such a way that
tracking device can "zero in" to it's initial location by first
finding one beacon that is in range, and then search for additional
beacons that are closer and closer based on the beacon map known to
the tracking device. Such an approach can also be used when the
tracking area is made up of several different rooms. Initially, the
room that the tracking device is determined and then the location
within the room can be found.
[0105] It is to be understood that the foregoing description is
intended to illustrate and not limit the scope of the invention,
which is defined by the scope of the appended claims. Other
aspects, advantages, and modifications are within the scope of the
following claims.
* * * * *