U.S. patent application number 16/192563 was filed with the patent office on 2021-03-04 for optical wind lidar-based multifunctional instrument for enhanced measurements and prediction of clear air turbulence and other wind-based aviation related phenomena.
This patent application is currently assigned to Ball Aerospace & Technologies Corp.. The applicant listed for this patent is Ball Aerospace & Technologies Corp.. Invention is credited to Jennifer H. Lee, Bevan D. Staple, Sara C. Tucker, Cynthia Wallace, Carl S. Weimer.
Application Number | 20210063429 16/192563 |
Document ID | / |
Family ID | 1000003796334 |
Filed Date | 2021-03-04 |
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United States Patent
Application |
20210063429 |
Kind Code |
A1 |
Tucker; Sara C. ; et
al. |
March 4, 2021 |
Optical Wind Lidar-Based Multifunctional Instrument for Enhanced
Measurements and Prediction of Clear Air Turbulence and Other
Wind-Based Aviation Related Phenomena
Abstract
A multiple functional instrument is provided. The instrument
includes an optical autocovariance function interferometer that can
feature multiple fields of view to detect winds in the atmosphere.
The instrument can include an infrared camera to detect atmospheric
temperatures and the presence of clouds, and a detector assembly
that detects the polarization of light returned to the
interferometer. Data collected by the instrument can be provided to
a deep and reinforcement learning algorithm for real-time
prediction of clear air turbulence and other wind-based aviation
safety phenomena. Moreover, predicted and actual conditions can be
correlated and used to train a deep learning algorithm to enable
more accurate predictions. The instrument can be carried by an
aircraft or other platform and operated to detect clear air
turbulence or other atmospheric phenomena, and to provide
instructions regarding flight parameters including wind-aided
navigation in order to minimize the effect of predicted
turbulence.
Inventors: |
Tucker; Sara C.; (Boulder,
CO) ; Staple; Bevan D.; (Longmont, CO) ; Lee;
Jennifer H.; (Boulder, CO) ; Wallace; Cynthia;
(Louisville, CO) ; Weimer; Carl S.; (Boulder,
CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ball Aerospace & Technologies Corp. |
Boulder |
CO |
US |
|
|
Assignee: |
Ball Aerospace & Technologies
Corp.
Boulder
CO
|
Family ID: |
1000003796334 |
Appl. No.: |
16/192563 |
Filed: |
November 15, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62723675 |
Aug 28, 2018 |
|
|
|
62723690 |
Aug 28, 2018 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01W 1/10 20130101; G06N
3/08 20130101; G08G 5/0039 20130101; G08G 5/0047 20130101; G01P
5/26 20130101; G01W 2001/003 20130101; G06N 20/00 20190101 |
International
Class: |
G01P 5/26 20060101
G01P005/26; G01W 1/10 20060101 G01W001/10; G08G 5/00 20060101
G08G005/00; G06N 3/08 20060101 G06N003/08; G06N 20/00 20060101
G06N020/00 |
Claims
1. A multifunctional instrument, comprising: a laser source; an
interferometer; a beam division mechanism, wherein the beam
division mechanism directs light from the laser source to a first
field of regard, wherein the beam division mechanism directs light
from the laser source to a second field of regard, wherein the beam
division mechanism directs light from within the first field of
regard to the interferometer, and wherein the beam division
mechanism directs light from within the second field of regard to
the interferometer.
2. The multifunctional instrument of claim 1, wherein the beam
division mechanism directs light from within the first field of
regard and light from within the second field of regard to the
interferometer at different times.
3. The multifunctional instrument of claim 1, wherein the laser
source outputs light having a first wavelength that is directed to
the first field of regard, wherein the laser source outputs light
having the same or a second wavelength that is directed to the
second field of regard, and wherein light from the first field of
regard and light from the second field of regard are provided to
the interferometer simultaneously.
4. The multifunctional instrument of claim 3, wherein the
interferometer provides a first optical path difference for light
of the first wavelength and a second optical path difference for
light of the second wavelength.
5. The multifunctional instrument of claim 1, further comprising:
an infrared camera, wherein the infrared camera has a field of view
that encompasses at least the first field of regard of the optical
autocovariance lidar.
6. The multifunctional instrument of claim 1, further comprising: a
plurality of detectors, wherein a first subset of the detectors
receives light of the first wavelength, wherein a second subset of
the detectors receives light of the second wavelength, wherein the
light received at a first detector of the first subset of detectors
is spaced in phase from the light received at a second detector of
the first subset of detectors by a nominal 90 degrees, and wherein
the light received at a first detector of the second subset of
detectors is spaced in phase from the light received at a second
detector of the second subset of detectors by a nominal 90
degrees.
7. A multifunctional instrument, comprising: a lidar system,
including: a first laser source; an interferometer; and detectors;
and a control system, including: memory, wherein a machine learning
algorithm is stored in the memory; and at least a processor,
wherein the processor is operable to execute the machine learning
algorithm, wherein wind measurement data is collected by the lidar
system and provided to the machine learning algorithm, wherein the
algorithm operates to predict turbulence or provide navigation
information from the wind measurement data.
8. The multifunctional instrument of claim 7, further comprising:
an accelerometer, wherein actual turbulence measurement data is
collected by the accelerometer and is provided as an input to the
machine learning algorithm.
9. The multifunctional instrument of claim 8, wherein the machine
learning algorithm is trained using correlated wind measurement
data and actual turbulence measurement data.
10. The multifunctional instrument of claim 7, wherein the
interferometer includes at least first and second fields of
regard.
11. The multifunctional instrument of claim 7, wherein the
interferometer includes: a first, forward looking field of regard;
a second, upward looking field of regard; and a third, downward
looking field of regard.
12. The multifunctional instrument of claim 7, further comprising:
an infrared camera.
13. The multifunctional instrument of claim 7, wherein the machine
learning algorithm is a deep neural network.
14. A method of detecting turbulence in the atmosphere, comprising:
making wind speed measurements along a series of angles centered
around the direction of travel of an aircraft; detecting turbulence
experienced by the aircraft; correlating the wind speed
measurements to the detected turbulence experienced by the
aircraft; and training a machine learning algorithm using the
correlated wind speed measurements and detected turbulence.
15. The method of claim 14, wherein the machine learning algorithm
is a deep neural network.
16. The method of claim 14, wherein the wind speed measurements are
obtained from a plurality of different ranges along the direction
of travel of the aircraft.
17. The method of claim 16, wherein the turbulence experienced by
the aircraft is detected by sensors carried by the aircraft, and
wherein correlating the wind speed measurements to the detected
turbulence includes at least one of spatial correlation, temporal
correlation, and amplitude correlation.
18. The method of claim 15, further comprising providing an output
from the deep neural network, wherein the output is a turbulence
prediction, wind-aided navigation information and a suggestion to
alter a flight parameter of the aircraft.
19. The method of claim 14, further comprising: taking wind
measurements along a plurality of look angles, wherein at least
some of the look angles do not correspond to the direction of
travel of the aircraft.
20. The method of claim 14, further comprising: determining a
strength of a return signal; in response to determining that the
strength of the return signal is low, increasing the range gate
length along which the wind speed measurements are made.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application Ser. No. 62/723,675, filed Aug. 28, 2018, and
the benefit of U.S. Provisional Patent Application Ser. No.
62/723,690, filed Aug. 28, 2018, the entire disclosures of which
are hereby incorporated herein by reference.
FIELD
[0002] The present disclosure is directed to systems and methods
for measuring and predicting wind-based aviation safety phenomena
including providing wind-aided navigation to an aircraft based on
data from multiple sources.
BACKGROUND
[0003] Severe wind conditions such as clear air turbulence
encounters by general and commercial aviation continue to pose
significant safety and flight efficiency concerns. Almost anyone
who has flown commercially has had an unpleasant experience with
turbulence and has a tale to tell about it. According to some
estimates, turbulence encounters account for well over 75% of all
weather-related injuries on commercial aircraft and amount to at
least $200M annually in costs due to passenger and crew injuries
and aircraft damage. Consequently, there is an urgent need to
provide accurate and real-time wind and turbulence predictions and
courses-of-action to meet the safety and navigation needs of
aviation communities.
[0004] However the real-time information about the current
turbulent state of the atmosphere required by pilots and
dispatchers for making tactical en-route decisions is not
adequately provided via the Federal Aviation Administration's
(FAA's) thunderstorm avoidance guidelines, by currently operational
turbulence forecasts, or future systems such as the Graphical
Turbulence Guidance (GTG) "Nowcast" (N-GTG) at the National Center
for Atmospheric Research (NCAR), which is slated to combine
turbulence observations, inferences and forecasts to produce new
turbulence assessments approximately every 15 minutes.
[0005] Moreover, despite the success of machine learning in a
variety of tasks, applications to the problem of weather
forecasting have been limited. Exceptions include the use of
Bayesian Networks for precipitation forecasts and temporal modeling
via Restricted Boltzmann Machines (RBM). To date, uses of machine
learning for weather prediction have been limited in that almost
all methods consider only one variable at a time and do not explore
the joint spatiotemporal statistics of multiple weather
phenomena.
[0006] Light detection and ranging (lidar) systems have been
developed that are capable of remotely measuring range-resolved
wind speeds for use in various applications, including but not
limited to wind-aided navigation of a platform, weather
forecasting, air quality prediction, air-traffic safety, and
climate studies. In general, lidar operates by transmitting light
from a laser source to a volume or surface of interest and
detecting the time of flight for the backscattered light to
determine a range to the scattering volume or surface.
[0007] A Doppler wind lidar also measures the Doppler frequency
shift experienced by the light scattered back to the instrument due
to the motions of molecules and aerosols (e.g. particles and
droplets) in the atmospheric scattering volumes, which is directly
tied to the speed of the wind in that volume, relative to the lidar
line of sight (LOS). The wind speed along the LOS is determined by
projecting the wind speed and direction (the wind vector) onto that
LOS.
[0008] One potential application for wind lidar systems is in
connection with the detection of atmospheric turbulence and wind
shear. As noted, atmospheric turbulence is a primary cause of
weather related injuries to aircraft passengers and flight crews.
Accordingly, detecting atmospheric turbulence is of great interest.
However, systems for detecting turbulence, and in particular clear
air turbulence, that can be carried by aircraft have been
unavailable. In particular, a system that was compact and that
provided a suitably wide field of view that could be deployed in a
conventional aircraft has been unavailable.
[0009] Moreover, most wind measurements consist of a single wind
Doppler lidar instrument. Such instruments generally have a narrow
field of view (FOV), limiting the area of surveillance.
Additionally, such instruments consist of a single wavelength,
which limits the data diversity for increasing the accuracy of
aviation safety weather-related predictions.
SUMMARY
[0010] Embodiments of the present disclosure overcome the
limitations described above by providing systems and methods
incorporating a multifunctional instrument that includes an optical
autocovariance wind lidar (OAWL) based instrument. In accordance
with at least some embodiments of the present disclosure, the wind
lidar based instrument is configured to perform wind measurements.
The wind lidar based instrument can also make measurements of
aerosol concentrations. In accordance with further embodiments of
the present disclosure, the multifunctional instrument includes a
camera or wide field of view infrared (IR) sensor for thermal
measurement of atmospheric behavior. The multifunctional instrument
can also include one or more on-board accelerometers, which can be
used to compare turbulence predictions to turbulence actually
encountered by an aircraft. As used herein, aircraft can include,
but are not limited to, airplanes, helicopters, airships (including
blimps), gliders, hot air balloons, stratospheric balloons, and
Unmanned Aerial Vehicles (UAVs) In accordance with further
embodiments of the present disclosure, a multifunctional instrument
is provided that includes a lidar system that is capable of
obtaining wind speed measurements and aerosol/particle
concentrations from multiple lines of sight. Moreover, in
accordance with at least some embodiments of the present
disclosure, measurements from multiple lines of sight can be made
simultaneously. Alternatively or in addition, a lidar capable of
making simultaneous measurements over multiple lines of sight as
described herein can include an interferometer that is configured
to operate at multiple wavelengths, and/or that can make wind and
aerosol concentration measurements simultaneously.
[0011] In accordance with still further embodiments of the present
disclosure, a multifunctional instrument is provided that
incorporates a processor and a deep learning algorithm. The deep
learning algorithm can be operated to collect, fuse, and correlate
data generated by the lidar alone or by the lidar and other sensors
included in the multifunctional instrument, to provide predictions
regarding turbulence in the atmosphere. Moreover, the deep learning
algorithm can be operated to alter or suggest alterations in the
course of an aircraft carrying the multifunctional instrument, or
of other aircraft.
[0012] Further embodiments of the present disclosure overcome the
limitations described above by providing unique and novel methods
for combining data fusion of multi-source information on which the
latest in artificial intelligence-based deep and reinforcement
learning processing algorithms are applied in a hybrid model to
provide accurate and real-time wind predictions for wind-aided
navigation of a platform, turbulence predictions, and
courses-of-actions to meet the needs of aviation communities.
[0013] Additional features and advantages of embodiments of the
disclosed systems and methods will become more readily apparent
from the following description, particularly when taken together
with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 depicts an aircraft carrying a multifunctional
instrument in accordance with embodiments of the present
disclosure;
[0015] FIG. 2 depicts components of a multifunctional instrument in
accordance with embodiments of the present disclosure;
[0016] FIG. 3 depicts components of an interferometer in accordance
with embodiments of the present disclosure; and
[0017] FIG. 4 depicts aspects of a process for applying deep
learning processing to detect and predict turbulence and other
atmospheric conditions in accordance with embodiments of the
present disclosure.
DETAILED DESCRIPTION
[0018] FIG. 1 depicts an aircraft 100 carrying a multifunctional
instrument or system 104 in accordance with embodiments of the
present disclosure. As used herein, an aircraft 100 can include,
but is not limited to, an airplane, an airship, a blimp, a glider,
a hot air balloon, a stratospheric balloon, a helicopter, and an
unmanned aerial vehicle (UAV). The multifunctional instrument 104
is capable of obtaining atmospheric measurements from within
different fields of regard 108. For example, and as discussed in
greater detail elsewhere herein, one or more lidars incorporating
an optical autocovariance interferometer can be included in the
multifunctional instrument 104 to obtain relative line of sight
wind speeds from selected ranges within different fields of regard
108 that intersect different target volumes 112. In addition, a
wide field of view (WFOV) infrared (IR) camera can be included in
the multifunctional instrument 104 for obtaining temperature
information at different locations within that device's field of
view 114.
[0019] More particularly, a lidar system included in a
multifunctional instrument 104 in accordance with embodiments of
the present disclosure can have multiple fields of regard 108, from
which relative line of sight wind speeds can be obtained at
selected ranges from the multifunctional instrument 104. These
different fields of regard 108 can include a forward looking field
of regard 108a, a downward looking field of regard 108b, and an
upward looking field of regard 108c. Although the different fields
of regard 108 depicted in the figure are shown at a spacing of
approximately 90 degrees from one another, different spacings are
possible. For example, the downward 108b and upward 108c facing
fields of regard 108 can be at angles of less than 90 degrees from
the forward-looking field of regard 108a. Moreover, additional
fields of regard, including side looking fields of regard, or
fields of regard spaced at angles of greater than 90 degrees, can
be provided. As can be appreciated by one of skill in the art after
consideration of the present disclosure, the lidar system operates
to transmit a beam of light as an output signal or beam 116 along
or within a corresponding field of view. The transmitted beam can
be scanned or varied in angle relative to the multifunctional
instrument 104 to collect data from within the field of regard 108.
Alternatively or in addition, a lidar system included in the
multifunctional instrument 104 can comprise an imaging or flash
lidar with a relatively large field of view that is coincident with
a corresponding field of regard 108, or that can be scanned within
the field of regard 108.
[0020] Particles in the atmosphere along the path of the
transmitted light reflect that light back to an interferometer
included in the lidar system. For example, at high altitudes (e.g.
above 20 km), molecules within a target volume 112 in the
atmosphere will backscatter at least some of the transmitted light
as a return signal 120. At lower altitudes (e.g. below 20 km)
molecules and aerosols within a target volume 112 in the atmosphere
will backscatter at least some of the transmitted light as a return
signal 120. The return signal 120 comprising at least some of the
backscattered light is received by the lidar system included in the
multifunctional instrument 104, and any Doppler shift experienced
by the light as a result of a relative line of sight wind speed at
a range corresponding to a target volume 112 can then be detected,
to determine the relative line of sight windspeed within that
target volume 112. This information can then be used to detect the
presence of turbulence 124, including but not limited to clear air
turbulence, in the target volume 112, and to obtain wind
measurements that can be used for wind-aided navigation of the
platform, weather forecasting, and the like. Moreover, wind
profiles based on wind measurements made by the multifunctional
instrument 104 at the aircraft 100 level and below can be provided
to global and local weather forecasting offices and systems in near
real-time to improve forecast model initialization.
[0021] In accordance with further embodiments of the present
disclosure, the polarization of light in the return signal 120 can
be used, alone or in combination with information received from
other sensors, to detect the presence of ice, ash, or dust
particles within the target volume 112. Although the detection of
turbulence and provision of aviation safety weather-related data
for an aircraft 100 carrying the instrument 104 and for use by
other aircraft or aviation safety information consumers is one
application of embodiments of the present disclosure, other
applications may include placing a multifunctional system 104 in
satellites, in space vehicles, in balloons, or in other vehicles or
locations, and with any number of different look angles in
different directions.
[0022] FIG. 2 depicts an arrangement of components of a
multifunctional instrument or system 104 in accordance with
embodiments of the present disclosure. In general, the
multifunctional instrument 104 includes a lidar system 204. The
lidar system 204 may be in the form of an optical autocovariance
wind lidar that incorporates a laser or light source 224 and an
interferometer 228. The laser 224 can output beams of light at
multiple wavelengths (.lamda..sub.1, .lamda..sub.2, . . .
.lamda..sub.n) within a time sequenced manner, or simultaneously.
Alternatively, multiple laser sources 224 operating at different
wavelengths can be provided. The lidar system 204 can include a
beam division system or mechanism 208 that operates to separate
output beams 116 of different wavelengths and direct the separated
beams 116 along different lines of sight within different fields of
regard 108. Moreover, the multifunctional instrument 104 can
include scan mirrors, variable optics, or other scan mechanisms 212
for scanning an output beam 116 across a target volume 112, and for
receiving return signals 120 from along selected lines of sight
within the field of regard 108 encompassing the target volume 112.
More particularly, the beam division system 208 operates to direct
light of different wavelengths along different paths. A scan
mechanism 212 can be provided for each of the different paths
(wavelengths). Accordingly, a scan mechanism 212a-c can direct a
respective beam of output light 116 along a selected look angle
within an associated field of regard 108a-c, and can further
operate to receive returns 120 from within the associated field of
regard 108. Accordingly, scanning mechanisms 212 can scan the
output beams 116 to obtain returns 120 from different locations
within a target volume 112, such that measurements of wind speed or
other phenomena can be made from select locations within the target
volume 112.
[0023] In accordance with at least some embodiments of the present
disclosure, the multifunctional instrument 104 includes components
for detecting a proportion of cross-polarized light in the return
signal 120. In such embodiments, the multifunctional instrument 104
can include a polarizing beam splitter 214 that sends co-polarized
light included in the return signal 120 to the interferometer 228,
and cross-polarized light to one or more detectors 215. More
particularly, one detector operable to determine an intensity of
the co-polarized light included in the interferometer 228 and one
detector 215 operable to determine an intensity of the
cross-polarized light is provided for each wavelength of
interest.
[0024] The multifunctional instrument 104 also includes a wide
field of view infrared camera 216. The infrared camera 216 can be
operated to obtain spatial and temporal temperature information
from within a relatively wide field of view 114. Moreover, the wide
field-of-view infrared camera can be pointed so as to encompass the
forward-looking field-of-regard 108a of the lidar (see, e.g., FIG.
1), and can be used to measure spatial and temporal temperatures
and atmospheric conditions such as turbulence. As an example, but
without limitation, the infrared camera 216 may comprise a wide
field of view infrared sensor for measuring the spatial and
temporal temperatures and atmospheric conditions such as turbulence
and providing a large area of surveillance over a wide wavelength
range (e.g. 7.5 to 14 .mu.m). For example, the infrared camera 216
can detect the presence of clouds and potential turbulent activity
along the direction of travel of the aircraft 100, and such
information can be used as an input for making aviation safety
weather-related predictions. In addition, such information can be
used to assist in steering an output beam 116 of the lidar system
204. As an alternative or in addition to an infrared camera 216, a
hyperspectral or multispectral instrument, including an instrument
with a wide field of view, can be included in the multifunctional
instrument 104.
[0025] In accordance with further embodiments of the present
disclosure, the multifunctional instrument 104 can include an
accelerometer 220, which can be operated to measure the intensity
of turbulence experienced by the aircraft 100, and to provide a
correlation between turbulence predictions made through operation
of the lidar system 204 and actual turbulence conditions
experienced by the aircraft 100.
[0026] Embodiments of the multifunctional instrument 104 described
herein additionally include an inertial navigation unit (INU) 232,
such as but not limited to a global positioning system (GPS) INU,
which can operate to provide aircraft 100 location information.
Such information can be used to support various functions,
including but not limited to geo-locating detected or predicted
aviation safety related weather conditions.
[0027] The various sensors and instruments such as the lidar system
204, the wide field of view camera 216, the accelerometer 220, the
beam division 208 and scanning 212 systems, and the related
mechanisms of the multifunctional instrument 104 can all be
interconnected to a control system 222. As discussed in greater
detail elsewhere herein, the various components can work in
conjunction with one another and the control system 222 to make
measurements of atmospheric conditions, and to make predictions
regarding the presence of turbulence in the atmosphere, including
but not limited to along the direction of motion of the aircraft
100, to correlate windspeed and temperature measurements and
related turbulence predictions to turbulence actually experienced
by the aircraft 100, to detect the presence of icing conditions, to
detect the presence of volcanic ash or other particles, and to
provide such or other information that is pertinent to aviation
safety or navigation or detected weather conditions, to other
aircraft, aviation safety related weather information consumers, or
general weather information consumers.
[0028] The control system 222 of the multifunctional instrument 104
can include various processing and operating components, including
but not limited to a processor 236, memory 240, and a
communications interface 244. As can be appreciated by one of skill
in the art after consideration of the present disclosure, the
processor 236 can include a general purpose programmable processor,
a graphics processing unit (GPU), a field programmable gate array
(FPGA), a controller, or a set of different processor devices or
chips. The memory 240 can include solid-state volatile or
non-volatile memory, such as flash memory, RAM, DRAM, SDRAM, or the
like. The memory 240 can also include various other types of memory
or other data storage devices, such as magnetic storage devices,
optical storage devices, or the like.
[0029] The processor 236 can generally operate to execute
programming code or instructions stored in the memory 240, for the
operation of the multifunctional instrument 104, including
coordination of the operation of components within the
multifunctional system 104. Moreover, the processor 236 can execute
application programming or instructions stored in the memory 240
for the onboard prediction of aviation safety related weather
conditions, and improved flight navigation paths including but not
limited to the detection of clear air turbulence along the path of
the aircraft 100. In accordance with still other embodiments of the
present disclosure, such predictions can be made in connection with
wind speed measurements taken by the lidar system 204 along lines
of sight other than those within the forward-looking field of
regard 108a, such as a downward looking field of regard 108b, or an
upward looking field of regard 108c. The measurements can provide
shear information related to potential turbulence or enhanced
aircraft navigation and fuel efficiency. Data collected or
generated by the sensors of the multifunctional instrument 104 can
be stored in the memory 240, presented to the crew of the aircraft
100, or communicated using the communication interface 244 to other
systems, such as aviation safety or navigation related weather
information consumers, other aircraft, weather services, or the
like.
[0030] An example of application programming or instructions that
can be stored in the memory 240 and executed by the processor 236
is a deep learning algorithm 242. The deep learning algorithm 242
can operate to collect, fuse, and correlate data generated by the
multifunction sensor 104, the infrared camera 216, the
accelerometers 220, and external sources. The deep learning
algorithm 242 can apply the data to make predictions regarding
turbulence and other wind-based aviation safety and efficiency
phenomena. This data can also be used to train the deep learning
algorithm 242 to enable increasingly accurate predictions of wind
based aviation safety phenomena or wind-aided navigation and
efficiency. In addition, embodiments of the present disclosure can
provide a deep learning algorithm 242 that can alter, or suggest
alterations in, the course of the aircraft 100, in order to avoid
turbulence or other wind based aviation safety or navigation
phenomena.
[0031] As previously noted, in at least some embodiments of the
present disclosure, the output beams 116 of the different fields of
regard 108 are associated with different wavelengths. In such
embodiments, an interferometer 228 capable of operating at
different wavelengths simultaneously can be used. The components of
such an interferometer 228 are depicted in FIG. 3. In this example,
a dual wavelength interferometer 228 is illustrated and described.
However, as can be appreciated by one of skill in the art after
consideration of the present disclosure, the interferometer 228 can
be configured to operate at a single wavelength or at more than two
wavelengths. In general, the interferometer 228 receives dual
wavelength light as an input. The light can comprise a time t0
sample of light output by the light source 224, and a time t>0
signal comprising the return signal 120 collected by the lidar
system 204. The light is passed to the interferometer 228 by a
transmission element 302, such as a fiber optic element and/or
turning mirror, that delivers light of a mix of different
polarizations to the interferometer 228. In accordance with
embodiments of the present disclosure, the interferometer system or
instrument 228 may include a first single or dual-wavelength
non-polarizing beam splitter 304 that directs or transmits a first
portion 308 of the received light to a first arm 312 and a second
portion 316 of the received light 300 to a second arm 320 of the
interferometer 228.
[0032] The first arm 312 includes a first reflective element 324
that is a first distance from the first non-polarizing beam
splitter 304. The first reflective element 324 reflects light of a
first wavelength 328 and transmits light of a second wavelength
332. The first reflective element 324, optionally in combination
with a secondary mirror 344, defines a first optical path length
for light of the first wavelength 328 included in the portion of
light directed to the first arm 312. In accordance with embodiments
of the present disclosure, the first reflective element 324 is a
frequency selective mirror or dichroic element. The first arm 312
further includes a second reflective element 336 that is a second
distance from the first non-polarizing beam splitter 304, where the
second distance is greater than the first distance. The second
reflective element 336 reflects light of the second wavelength 332.
The second reflective element 336, optionally in combination with
the same secondary mirror 344, defines a second optical path length
for light of the second wavelength 332 included in the portion of
light directed to the first arm 312.
[0033] The second arm 320 includes a third reflective element 340
that is a third distance from the first non-polarizing beam
splitter 304, where the third distance is less than either of the
first and second distances. The third reflective element 340,
optionally in combination with a secondary mirror 348, defines a
third optical path length for the light of the first and second
wavelengths included in the portion of the light directed to the
second arm 320.
[0034] The first 312 and second 320 arms may be configured as
cat-eye assemblies with reflective elements 324, 336, and 340 that
comprise non-planar, for example parabolic, mirrors that are
combined with secondary mirrors 344 and 348 to provide a compact
physical structure that provides an optical path difference for
rays within a given one of the arms 312 and 320 that is essentially
constant for all rays of a given wavelength within the field of
view of the interferometer 228, regardless of the angle at which
the rays entered the assembly. Systems and methods for providing
such a field widening lens are described in U.S. Pat. No.
7,929,215, the contents of which are incorporated herein by
reference in their entirety.
[0035] In accordance with further embodiments of the present
disclosure, one of the arms 312 or 320 of the interferometer 228
includes a quarter wave plate 352 for introducing a delay to light
of a linear polarization. The quarter wave plate 352 can be in, for
example, the optical path traversed by the light directed along the
first arm 312 of the interferometer 228.
[0036] Light at one or both wavelengths from the first 312 and
second 320 arms is combined at a second non-polarizing beam
splitter 356. A first portion 360 of the combined light is directed
(e.g. is passed) by the second non-polarizing beam splitter 356 to
a first wavelength selective or dichroic element 364, while a
second portion 368 of the combined light is directed (e.g. is
reflected) by the second non-polarizing beam splitter 356 to a
second wavelength selective or dichroic element 372.
[0037] Light of the first wavelength is reflected by the first
wavelength selective element 364 to a first polarizing beam
splitter 376a, while light of the second wavelength is passed by
the first wavelength selective element 364 to a second polarizing
beam splitter 376b. Light of the first wavelength is reflected by
the second wavelength selective element 372 to a third polarizing
beam splitter 376c, while light of the second wavelength is passed
by the second wavelength selective element 372 to a fourth
polarizing beam splitter 376d. In accordance with embodiments of
the present disclosure, each of the first through fourth polarizing
beam splitters 376 is associated with first and second detectors
380. Moreover, a portion of the light received at each of the
detectors has been delayed by a selected amount within the
instrument relative to other light. The detectors 380 may comprise
photodetectors that are operative to detect an amplitude
(intensity) of light incident thereon. Moreover, the detector
electronics assemblies 380 can be selected and configured to
operate at speeds that are fast enough to resolve returns from
different ranges, and thus from different portions of the target
volume 112.
[0038] Specifically, light of the first wavelength that has
traversed the first path length in the first arm 312 is combined
with the light of the first wavelength that has traversed the third
path length in the second arm 320, thus creating an interference
pattern. The intensity of the interference pattern is measured at
each of the detectors 380 associated with the first 376a and third
376c polarizing beam splitters, where the phase of the signals
received at each of the detectors 380 are, through the combination
of transmitting and reflecting elements within the interferometer
228, spaced in phase from neighboring signals of the same
wavelength by 90 degrees. Similarly, light of the second wavelength
that has traversed the second path length in the first arm 312 is
combined with the light of the second wavelength that has traversed
the third path length in the second arm 320, and the intensity of
the interference pattern is measured by each of the detectors 380
associated with the second 376b and fourth 376d polarizing beam
splitters, where the interference pattern signals received at each
of the detectors 380 are spaced in phase from the other signals of
the same wavelength by a nominal 90 degrees. Analysis can then be
performed on the signals from each set of detectors (one set per
wavelength) to determine a phase of the interferometer fringe
(measured autocovariance function) of the light relative to the
four detector phase positions. More particularly, the phase
analysis procedure can be performed for each of the wavelengths at
times t0 and t>0 to determine a relative phase change of the
interferometer fringe (measured autocovariance function) of the
light, from which a line of sight velocity of the atmospheric
constituents from which the return light 120 was reflected may be
retrieved. As can be appreciated by one of skill in art after
consideration of the present disclosure, the measured relative
phase change can then be used to determine the relative line of
sight wind speed within the target volume 112 at a selected
range.
[0039] In accordance with further embodiments of the present
disclosure, the polarization of light received as part of a return
signal 120 can be determined. In such embodiments, the transmitted
beam 116 may be controlled to have a selected polarization. The
intensity or amount of co-polarized light relative to the intensity
or amount or cross-polarized light in the return signal 120 can
then be determined for at least one of the wavelengths of light in
the return signal 120. For example, a polarizing beam splitter 214
can be provided to divide light included in the return signal 120
into a co-polarized portion that is provided to the interferometer
228, and a cross-polarized portion that is provided to a detector
215. A large proportion of cross polarized light relative to
co-polarized light in the return signal 120 indicates that ice,
ash, or dust particles are present within the target volume 112.
These measurements can be correlated with temperature measurements,
for example taken by the infrared camera 216, to indicate the
presence of icing conditions, volcanic ash, or other relevant
conditions. Different proportions of cross polarized and
co-polarized light (into the interferometer) in the return signal
120 can also indicate aerosol properties within the target volume
112.
[0040] As depicted, the multifunctional instrument 104 can be
associated with multiple fields of regard, with multiple pointing
angles of the lidar beam being included within each field of
regard. For example, a first field of regard 108a can be directed
so as to obtain measurements from ahead of the aircraft 100. This
first field of regard 108a can operate in connection with an output
beam 116 having a first wavelength. An example of a suitable
wavelength is 355 nm, which is suitable for measuring winds and
clear air turbulence in a direction forward of the aircraft 100
motion. A second field of regard 108b can be pointed in a downward
direction, to obtain measurements from altitudes below the aircraft
flight altitude. This second field of regard 108b can operate in
connection with an output beam 116 having a second wavelength. An
example of a suitable wavelength for a downward looking field of
regard 108b is 1.5 .mu.m, which is suitable for measuring winds in
regions with higher aerosol/particle concentration including in
clouds. A third field of regard 108c can be pointed upward, to
obtain measurements from higher altitudes. This third field of
regard 108c can operate in connection with an output beam 116c
having a third wavelength. Example of suitable wavelengths for an
upward looking field of regard 108c are 355 nm and 532 nm, both of
which are suitable for measuring returns produced by molecules at
high altitudes. Alternatively, the operational wavelengths can be
limited to those that comply are eye-safe. Moreover, the
transmitted beam associated with a given field of regard 108 can be
scanned to widen the area from which measurements are taken.
[0041] In accordance with other embodiments of the present
disclosure, measurements of wind speed within target volumes 112
associated with different fields of regard 108 can be obtained in a
time sequenced manner, rather than simultaneously. Moreover, in
accordance with at least some embodiments of the present
disclosure, an interferometer 228 that provides different optical
path differences to light of different wavelengths is not required.
Measurements taken by the lidar system 204 can be used in
combination with measurements taken by the wide angle infrared
camera 216. Moreover, measurements taken by one of the instruments
204 or 216 can be used to determine operating parameters of the
other instrument. For example, the look angle of the lidar system
204 can be selected based on the determined location of clouds
detected by the infrared camera 216.
[0042] In accordance with embodiments of the present disclosure, in
addition to a forward pointing field of regard 108a, information
relative to turbulence that might affect the aircraft 100 can be
obtained from downward looking 108b and/or upward looking 108c
fields of view. For example, turbulence is indicated by the
presence of different winds having different directions at
different, adjacent altitudes. In addition, by enabling the
detection of wind speeds at altitudes above and below the aircraft
100, embodiments of the present disclosure can facilitate the
selection of an altitude at which a tailwind component is present,
facilitating fuel efficiency and speed.
[0043] In accordance with still other embodiments of the present
disclosure, actual turbulence experienced by the aircraft 100, as
measured by one or more accelerometers 220, can be used to validate
and/or refine the predictions made based on measurements taken by
the other components of the multifunction system 104. In addition
to measurements taken by the multifunctional system 104 directly,
weather information from other sources that may lead to turbulence
can be validated based on the indication of turbulence experienced
by the aircraft 100.
[0044] FIG. 4 depicts a process for applying deep learning
processing to detect turbulence in accordance with embodiments of
the present disclosure. The process can be implemented by execution
by the processor 236 of the deep learning algorithm 242 stored in
memory 240. The process includes receiving and processing inputs
from multiple data sources (step 404). These data sources can
include inputs from a multifunctional system or instrument 104.
Specific examples of input data include, but are not limited to,
OAWL 204 based remotely sensed wind vector and clear air turbulence
measurements, IR camera 216 based measurements of clouds, on board
turbulence intensity level detection signals from sensors, such as
accelerometers and aircraft eddy dissipation rate measurements, and
external weather and turbulence forecasting data, such as graphical
turbulence guidance product (GTG) and now casting (e.g. NGTG).
[0045] Algorithmic input data fusion is then performed (step 408).
Data fusion can include correlating turbulence predictions made by
execution of the deep learning algorithm 242 based on measurements
by the lidar system 204 or other multifunctional instrument 104
sensors with actual turbulence encountered by the aircraft 100, for
example as indicated by onboard accelerometers 220, or other
sensors. In addition to temporal correlation, data fusion can
include correlating the severity of the predicted turbulence to the
severity of the turbulence detected by the aircraft at various
altitudes and relative air speeds of turbulence encounters. Other
examples of data fusion include correlating external weather and
turbulence forecasting data with turbulence predictions made by the
multifunction system 104 and/or actual turbulence measurements made
by sensors included in the multifunction system 104. In accordance
with further embodiments of the present disclosure, data fusion
relative to atmospheric conditions other than or in addition to
clear air turbulence can be performed. For example, correlations
between predictions regarding icing conditions, the presence of
organic ash, or other particles in the atmosphere and the
conditions actually encountered by the aircraft 100 can be made.
Data regarding the polarization of backscattered return laser light
120 and the detection of clouds using the infrared camera 216 are
examples of sources of data regarding predictions of such other
atmospheric conditions.
[0046] In a training mode, fused data can be used to train a deep
learning model implemented by the learning algorithm 242 (step
412). In deep learning, the fused data is fed into the model and
used to refine the predictions made regarding the atmospheric
parameters of interest, such as clear air turbulence. More
particularly, by comparing the data used to make the predictions
with the actual turbulence measurements, refinements to the model
or algorithm 242 to increase the accuracy of the predictions can be
made. For instance, if the model implemented by the algorithm 242
predicts that turbulence of a certain predicted severity will be
encountered at a particular range, based on measurements made by
the multifunction sensor 104 from or about that range, the actual
severity of any turbulence encountered when the aircraft 100 has
reached the site of the predicted turbulence can be used to adjust
the model of the algorithm 242 so that future predictions are more
accurate. As can be appreciated by one of skill in the art after
consideration of the present disclosure, actual turbulence
measurements made by sensors, such as accelerometers 220 carried by
the aircraft 100 as part of the multifunction sensor 104, can be
located temporally, using clock information, and spatially, using
the geolocation data, for example from an INU 232 included in the
multifunction sensor 104. Such predictions can be refined to
include characteristics of the turbulence predicted by the
multifunction sensor 104 and the effects of turbulence on the
particular aircraft 100 at various altitudes and air-speeds.
Alternatively or in addition, turbulence predictions based on
external weather and turbulence forecasting data alone or in
combination with the sensor data regarding actual turbulence can be
refined through the training of the algorithm 242. In accordance
with embodiments of the present disclosure, the machine learning
process includes supervised learning, with the algorithm 242 being
trained to accurately detect the presence and severity of
turbulence or other aviation safety weather-related parameters. In
accordance with still other embodiments the present disclosure, the
machine learning process can include reinforcement learning, in
which feedback regarding the accuracy of predictions made from
input data by the algorithm 242 is checked against measurements of
actual instantiations of the predicted phenomenon, to allow the
algorithm 242 performance to be continually improved.
[0047] The training process results in deep learning models that
are better able to predict, based on the various inputs, such as
data collected by the multifunction sensor 104 alone or in
combination with data from external weather forecasting services or
other instruments, the present clear air turbulence, or other
weather conditions of interest. Accordingly, at step 416, the
trained deep learning model 242 can be applied to predict output
information with improved accuracy. The output information can
comprise deep learning real time output data (step 420), which can
include enhanced wind vector values, enhanced clear air turbulence
intensity values, and enhanced correlation between OAWL turbulence
detection by the multifunction sensor 104 and the sensing by
accelerometers 220 as the aircraft 100 flies through the turbulent
path.
[0048] In accordance with further embodiments of the present
disclosure, the reinforcement learning models can be used to
incorporate the fused data, and predict optimized courses of action
for the aircraft 100 and/or the multifunction sensor 104 (step
424). This reinforcement learning real time output data (step 428)
can include action to change the laser range gate for measurements
made by the lidar system 204, and action to inform a pilot or an
autopilot system to change flight parameters or to stay the course
as the best reaction to predicted weather conditions.
[0049] The reinforcement learning models or algorithms 242 can
include a number of different variants, such as Q-Learning,
State-Action-Reward-State-Action (SARSA), Deep Q Network (DQN), and
Deep Deterministic Policy Gradient (DDPG).
[0050] The execution of algorithms 242 implementing the deep
learning model for processing data input from the multifunction
sensor 104 and other data sources can be performed by the processor
236 included in the multifunctional instrument 104. Accordingly,
embodiments of the present disclosure provide an onboard processing
solution. In addition, turbulence and other pertinent weather
information can be provided in real time or near real-time (e.g.
after a processing delay of less than one second), to enable the
flight parameters of an aircraft 100 to be adjusted in response to
the predicted weather conditions. Moreover, the output of the
algorithm 242 can include instructions or suggestions regarding
actions in the form of flight parameters adjustments that can be
made to minimize the effect of the predicted weather condition. In
addition to increasing the accuracy of predictions through
training, embodiments of the present disclosure enable the
integration and fusion of data from multiple sources to further
increase the accuracy of weather forecasting information, including
predictions of clear air turbulence, provided by the algorithm
242.
[0051] Embodiments of the present disclosure can therefore include
a multifunctional instrument 104 that incorporates optical
autocovariance wind lidar-based instruments in combination with
wide field of view cameras or sensors. For example, a lidar system
204 comprising an OAWL instrument having multiple lines of sight
108 can be included in the multifunctional instrument 104 for wind
measurement and aerosol characterization, and a wide field of view
IR sensor 216 can be included in the multifunctional instrument 104
for thermal measurement of atmospheric behavior. In accordance with
still other embodiments, a multifunctional instrument 104 can
additionally include on-board turbulence intensity level detection
instruments, such as one or more accelerometers 220 for measuring
turbulence for an aircraft 100 which includes platforms not limited
to airplanes, helicopters, airships (including blimps), gliders,
hot air balloons and Unmanned Aerial Vehicles (UAVs) carrying the
multifunctional instrument 104. Accordingly, a multifunctional
instrument 104 as described herein can provide enhanced
measurements of clear air turbulence and other weather-based
aviation safety and navigation phenomena (volcanic ash, icing
conditions). Moreover, by incorporating more than one instrument or
sensor, a multifunctional instrument 104 as described herein
provides multi-source, diverse data sets for increasing the
accuracy of and finding correlations in aviation safety
weather-related predictions, and for providing wind-aided
navigation information and guidance.
[0052] In addition, a multifunctional instrument 104 in accordance
with embodiments of the present disclosure can provide multiple
field of regard 108 OAWL instrument configurations which may
incorporate multiple wavelengths, increasing the data diversity for
aviation safety weather-related predictions. As examples, but
without limitation, a lidar system 204 included in a
multifunctional instrument 104 in accordance with embodiments of
the present disclosure can feature a first field of regard 108a
comprising a horizontal LOS for measuring winds and clear air
turbulence using an output beam 116a having a first wavelength
(.lamda..sub.1)(e.g., 355 nm), a second field of regard 108b
comprising a down-looking LOS for measuring winds in clouds using
an output beam 116b having a second wavelength (.lamda..sub.2)
(e.g., 1.5 micron), and a third field of regard 108c comprising an
up-looking LOS for measuring winds using an output beam 116c having
a third wavelength (.lamda..sub.3) (e.g., 532 nm). In addition,
some or all of the fields of regard 108 can be associated with or
established by a scanning mechanism 212, such as but not limited to
a conical scan mechanism that varies a field of view or a line of
sight of a lidar system 204.
[0053] In addition to one or more lidar systems 204, a
multifunctional instrument 104 in accordance with embodiments of
the present disclosure can include a wide FOV IR sensor or camera
216 for measuring the spatial and temporal temperatures in
atmospheric conditions such as turbulence and providing a large
area of surveillance over a wide wavelength band (e.g., 7.5 to 14
microns) for supporting weather-related predictions for aviation
safety. Alternatively, or in addition, a camera 216 can be utilized
to detect cloud formations or other phenomena, to enable the lidar
system 204 to scan areas without cloud formations. Moreover,
on-board turbulence intensity level detection sensors or
accelerometers 220 can be included to provide correlation between
OAWL turbulence detection and the amplitude of its impact for the
given altitude and air speed conditions based on what is sensed as
the aircraft 100 flies through the turbulent path.
[0054] Still further embodiments of the present disclosure provide
a multifunctional instrument 104 that provides unique and novel
methods of combining advanced data fusion, deep learning, and
reinforcement learning algorithms simultaneously into a hybrid
model to provide measurements from the atmosphere, and predicted
conditions based on such measurements. For example, a
multifunctional instrument 104 in accordance with embodiments of
the present disclosure can include a deep learning algorithm 242
that, based on information from sensors included in the
multifunctional instrument 104, provide as outputs enhanced wind
vector values, enhanced clear air turbulence intensity values,
enhanced correlation between OAWL turbulence detection and sensing
by accelerometers 220 as the aircraft 100 flies through the
turbulent path, and/or reinforcement learning-based optimized
course of action, such as actions to change a laser range gate of a
lidar system 204, to inform a pilot or auto pilot to change a
flight path or stay the course, and the like. Moreover, as can be
appreciated by one of skill in the art after consideration of the
present disclosure, optimization of a lidar system 204 range gate
enhances the efficient collection of data. For instance, when the
strength of a return signal 120 is low, the range gate length along
which wind speed measurements are made can be increased, which
decreases range resolution, but increases sensitivity.
[0055] Embodiments of the present disclosure incorporating deep
learning can provide continuously learned spatial and temporal
analytics. In particular, the model implemented by the algorithm
242 can identify and learn from recurring weather patterns over
time including weather patterns in the form of data collected by a
multifunctional instrument 104 as described herein. The deep
learning can also address spatial correlation, including the
spatial dynamic influence of atmospherics on weather phenomena and
associated predictions provided as output by the algorithm 242.
Embodiments of the present disclosure can additionally incorporate
reinforcement learning. As a result, embodiments of the present
disclosure can provide for optimized courses of action in guidance
provided regarding an optimal flight path for an aircraft 100, and
for the operation of instruments and sensors included in a
multifunctional instrument 104 as disclosed herein.
[0056] Although various embodiments of a multifunctional instrument
104 having particular features have been described, other
configurations are possible. For example, different interferometer
228 configurations can be incorporated into the multifunctional
instrument 104. For instance, rather than incorporating a single
interferometer 228 capable of operating at a plurality of
wavelengths, a plurality of interferometers 228 that each operate
at a single wavelength can be included. As another example,
interferometers for handling different wavelengths and that are
capable of handling different numbers of wavelengths can be
included in any combination.
[0057] As another example, an interferometer 228 included in a
multifunctional instrument in accordance with embodiments of the
present disclosure need not incorporate a field widening lens
arrangement. For instance, rather than a set of mirrors, the
interferometer 228 can include a hexagonal beam splitter.
[0058] The information available from a multifunctional instrument
104 as described herein can include data collected from returns at
multiple wavelengths indicating the presence, magnitude, and
direction of atmospheric winds, from within multiple fields of
regard at different angles relative to the instrument. The data can
additionally include information regarding the presence of ice,
ash, or dust particles in the atmosphere. Moreover, information
regarding the presence and location of clouds can be obtained. The
data collected by the multifunctional instrument 104 can be
processed using artificial intelligence-based deep and
reinforcement learning processing algorithms 242 to provide
real-time and near-real time weather predictions, wind-aided
navigation, turbulence predictions, and/or courses of action for
use by an aircraft 100 carrying the multifunctional instrument 104,
by other aircraft, or by other data consumers. Predictions and
forecasts regarding measurements made using remote sensing
instruments included in the multifunctional instrument 104 can be
validated against measurements of actual conditions, for example as
detected by other sensors or instruments, including but not limited
to an accelerometer 220 or the perceptions of a pilot of the
aircraft 100. Moreover, validation results can be used to refine
the training and operation of the algorithm 242. In addition to
providing information useful to ensuring a smooth and safe flight,
prediction and measurements made by a multifunctional instrument
104 in accordance with embodiments of the present disclosure can
aid in efficiency, for instance by assisting the aircraft 100 in
locating altitudes at which favorable wind conditions are
present.
[0059] The foregoing discussion of the disclosed systems and
methods has been presented for purposes of illustration and
description. Further, the description is not intended to limit the
disclosed systems and methods to the forms disclosed herein.
Consequently, variations and modifications commensurate with the
above teachings, within the skill or knowledge of the relevant art,
are within the scope of the present disclosure. The embodiments
described hereinabove are further intended to explain the best mode
presently known of practicing the disclosed systems and methods,
and to enable others skilled in the art to utilize the disclosed
systems and methods in such or in other embodiments and with
various modifications required by the particular application or
use. It is intended that the appended claims be construed to
include alternative embodiments to the extent permitted by the
prior art.
* * * * *