U.S. patent application number 17/354324 was filed with the patent office on 2022-04-21 for techniques to compensate for mirror doppler spreading in coherent lidar systems using matched filtering.
The applicant listed for this patent is AEVA, INC.. Invention is credited to Jose Krause Perin, Rajendra Tushar Moorti, Mina Rezk, Kumar Bhargav Viswanatha.
Application Number | 20220120860 17/354324 |
Document ID | / |
Family ID | 1000005724091 |
Filed Date | 2022-04-21 |
United States Patent
Application |
20220120860 |
Kind Code |
A1 |
Krause Perin; Jose ; et
al. |
April 21, 2022 |
TECHNIQUES TO COMPENSATE FOR MIRROR DOPPLER SPREADING IN COHERENT
LIDAR SYSTEMS USING MATCHED FILTERING
Abstract
A received signal is sampled at the LiDAR system and the
received signal is converted to a frequency domain, where the
received signal comprises a first frequency waveform. A matched
filter is selected, where the matched filter comprises a second
frequency waveform with a set of coefficients to match the first
frequency waveform. The set of coefficients are updated according
to a set of metrics. The received signal is filtered by the matched
filter to generate a filtered received signal. Range and velocity
information is extracted from the filtered received signal.
Inventors: |
Krause Perin; Jose;
(Mountain View, CA) ; Rezk; Mina; (Haymarket,
VA) ; Viswanatha; Kumar Bhargav; (Santa Clara,
CA) ; Moorti; Rajendra Tushar; (Mountain View,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AEVA, INC. |
Mountain View |
CA |
US |
|
|
Family ID: |
1000005724091 |
Appl. No.: |
17/354324 |
Filed: |
June 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
63093599 |
Oct 19, 2020 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01S 7/4808 20130101;
G01S 17/58 20130101; G01S 17/08 20130101; G01S 7/4817 20130101 |
International
Class: |
G01S 7/48 20060101
G01S007/48; G01S 7/481 20060101 G01S007/481; G01S 17/08 20060101
G01S017/08; G01S 17/58 20060101 G01S017/58 |
Claims
1. A method in a light detection and ranging (LiDAR) system,
comprising: sampling a received signal at the LiDAR system and
converting the received signal to a frequency domain, wherein the
received signal comprises a first frequency waveform; selecting a
matched filter, the match filter comprising a second frequency
waveform with a set of coefficients to match the first frequency
waveform; updating the set of coefficients according to a set of
metrics; filtering the received signal by the matched filter to
generate a filtered received signal; and extracting range and
velocity information from the filtered received signal.
2. The method of claim 1, wherein selecting a matched filter
comprises selecting a rectangular waveform, a sinc waveform, a sinc
squared waveform, or a Gaussian waveform to be the second frequency
waveform.
3. The method of claim 1, wherein the set of coefficients are
updated according to at least one of an angular speed of a scanning
mirror, a position of the scanning mirror, a geometry of an optical
scanner, or the target.
4. The method of claim 1, wherein the set of coefficients are
updated such that a filter bandwidth is proportional to at least
one of an angular speed of a scanning mirror, a scanning mirror
size, or a beam diameter.
5. The method of claim 1, wherein the second frequency waveform is
determined based on an estimation of a power spectrum density
function of the received signal.
6. The method of claim 1, wherein the set of coefficients are
updated based on a change in a hardware configuration or a system
operation including a change of a mirror angular speed or a scan
pattern change.
7. The method of claim 1, further comprising inputting the filtered
received signal into a peak selection process to extract the range
and velocity information.
8. The method of claim 1, wherein the second frequency waveform is
determined based on a model or a simulation or a measurement of an
optical sub-system of the LiDAR system.
9. A light detection and ranging (LiDAR) system, comprising: a
memory; a processor, operatively coupled with the memory, to:
sample a received signal at the LiDAR system and convert the
received signal to a frequency domain, wherein the received signal
comprises a first frequency waveform; select a matched filter, the
match filter comprising a second frequency waveform with a set of
coefficients to match the first frequency waveform; update the set
of coefficients according to a set of metrics; filter the received
signal by the matched filter to generate a filtered received
signal; and extract range and velocity information from the
filtered received signal.
10. The LiDAR system of claim 9, wherein the second frequency
waveform comprises a rectangular waveform, a sinc waveform, a sinc
squared waveform, or a Gaussian waveform to be the second frequency
waveform.
11. The LiDAR system of claim 9, wherein the set of coefficients
are updated according to at least one of an angular speed of a
scanning mirror, a position of the scanning mirror, a geometry of
an optical scanner, or the target.
12. The LiDAR system of claim 9, wherein the set of coefficients
are updated such that a filter bandwidth is proportional to at
least one of an angular speed of a scanning mirror, a scanning
mirror size, or a beam diameter.
13. The LiDAR system of claim 9, wherein the second frequency
waveform is determined based on an estimation of a power spectrum
density function of the received signal.
14. The LiDAR system of claim 9, wherein the set of coefficients
are updated based on a change in a hardware configuration or a
system operation including a change of a mirror angular speed or a
scan pattern change.
15. The LiDAR system of claim 9, wherein the processor operatively
coupled with the memory is further to input the filtered received
signal into a peak selection process to extract the range and
velocity information.
16. The LiDAR system of claim 9, wherein the second frequency
waveform is determined based on a model or a simulation or a
measurement of an optical sub-system of the LiDAR system.
17. A non-transitory machine-readable medium having instructions
stored therein, which when executed by a processor of a light
detection and ranging (LiDAR) system, cause the processor to:
sample a received signal at the LiDAR system and convert the
received signal to a frequency domain, wherein the received signal
comprises a first frequency waveform; select a matched filter, the
match filter comprising a second frequency waveform with a set of
coefficients to match the first frequency waveform; update the set
of coefficients according to a set of metrics; filter the received
signal by the matched filter to generate a filtered received
signal; and extract range and velocity information from the
filtered received signal.
18. The non-transitory machine-readable medium of claim 17, wherein
the second frequency waveform comprises a rectangular waveform, a
sinc waveform, a sinc squared waveform, or a Gaussian waveform to
be the second frequency waveform.
19. The non-transitory machine-readable medium of claim 17, wherein
the set of coefficients are updated according to at least one of an
angular speed of a scanning mirror, a position of the scanning
mirror, a geometry of an optical scanner, or the target.
20. The non-transitory machine-readable medium of claim 17, wherein
the set of coefficients are updated such that a filter bandwidth is
proportional to at least one of an angular speed of a scanning
mirror, a scanning mirror size, or a beam diameter.
21. The non-transitory machine-readable medium of claim 17, wherein
the second frequency waveform is determined based on an estimation
of a power spectrum density function of the received signal.
22. The non-transitory machine-readable medium of claim 17, wherein
the set of coefficients are updated based on a change in a hardware
configuration or a system operation including a change of a mirror
angular speed or a scan pattern change.
23. The non-transitory machine-readable medium of claim 17, wherein
the processor operatively coupled with the memory is further to
input the filtered received signal into a peak selection process to
extract the range and velocity information.
24. The non-transitory machine-readable medium of claim 17, wherein
the second frequency waveform is determined based on a model or a
simulation or a measurement of an optical sub-system of the LiDAR
system.
Description
RELATED APPLICATIONS
[0001] This application claims priority from and the benefit of
U.S. Provisional Patent Application No. 63/093,599 filed on Oct.
19, 2020, the entire contents of which are incorporated herein by
reference in their entirety.
TECHNICAL FIELD
[0002] The present disclosure relates generally to light detection
and ranging (LiDAR) systems, for example, techniques to compensate
for mirror Doppler spreading in coherent LiDAR systems.
BACKGROUND
[0003] Frequency-Modulated Continuous-Wave (FMCW) LiDAR systems
include several possible phase impairments such as laser phase
noise, circuitry phase noise, flicker noise that the driving
electronics inject on a laser, drift over temperature/weather, and
chirp rate offsets. A scanning FMCW LiDAR system may use a moving
scanning mirror to steer light beams and scan a target or a target
environment. To achieve a wide field of view and high frame rates,
the scanning mirror may have a high angular speed. The high mirror
angular speed may cause several impairments. For example, the
mirror-induced Doppler shift may broaden the received signal
bandwidth. The received signal intensity may be lowered, and
consequently the detection probability may be reduced. Thus, the
error in range, velocity, and reflectivity measurements may be
increased.
SUMMARY
[0004] The present disclosure describes various examples, without
limitation, methods of processing received signal in LiDAR
systems.
[0005] In some examples, disclosed herein is a method of processing
a received signal by a matched filter, for example, to compensate
for mirror Doppler spreading. The received signal may be filtered
by the matched filter based on an expected received signal shape or
waveform. For example, the received signal in the frequency domain
(or "input spectrum") may be filtered by the matched filter that
aims to match the expected received signal power spectrum density
(PSD). The filter coefficients may be constant (e.g., derived from
theoretical simulation or modeling), or updated depending on key
factors such as a mirror angular speed, a mirror position, a
scanner geometry, a target, a scene, etc. As the detection occurs
at the point where SNR is maximized, the method may result in more
accurate frequency and energy measurements.
[0006] In some examples, a method in a LiDAR system is disclosed
herein. A received signal is sampled at the LiDAR system and the
received signal is converted to a frequency domain, where the
received signal comprises a first frequency waveform. A matched
filter is selected, where the matched filter comprises a second
frequency waveform with a set of coefficients to match the first
frequency waveform. The set of coefficients are updated according
to a set of metrics. The received signal is filtered by the matched
filter to generate a filtered received signal. Range and velocity
information is extracted from the filtered received signal.
[0007] In some examples, a LiDAR system is disclosed herein. The
LiDAR system comprises a memory and a processing device or
processor operatively coupled with the memory. The processing
device or processor is to sample a received signal at the LiDAR
system and convert the received signal to a frequency domain, where
the received signal comprises a first frequency waveform. The
processing device or processor is further to select a matched
filter, where the matched filter includes a second frequency
waveform with a set of coefficients to match the first frequency
waveform. The processing device or processor is further to update
the set of coefficients according to a set of metrics, and to
filter the received signal by the matched filter to generate a
filtered received signal. The processing device or processor is
further to extract range and velocity information from the filtered
received signal.
[0008] In some examples, a non-transitory machine-readable medium
is disclosed herein. The non-transitory machine-readable medium has
instructions stored therein, which when executed by a processing
device or processor of a LiDAR system, cause the processing device
or processor to sample a received signal at the LiDAR system and
convert the received signal to a frequency domain, where the
received signal comprises a first frequency waveform. The
processing device or processor is further to select a matched
filter, where the matched filter includes a second frequency
waveform with a set of coefficients to match the first frequency
waveform. The processing device or processor is further to update
the set of coefficients according to a set of metrics, and to
filter the received signal by the matched filter to generate a
filtered received signal. The processing device or processor is
further to extract range and velocity information from the filtered
received signal.
[0009] These and other aspects of the present disclosure will be
apparent from a reading of the following detailed description
together with the accompanying figures, which are briefly described
below. The present disclosure includes any combination of two,
three, four or more features or elements set forth in this
disclosure, regardless of whether such features or elements are
expressly combined or otherwise recited in a specific example
implementation described herein. This disclosure is intended to be
read holistically such that any separable features or elements of
the disclosure, in any of its aspects and examples, should be
viewed as combinable unless the context of the disclosure clearly
dictates otherwise.
[0010] It will therefore be appreciated that this Summary is
provided merely for purposes of summarizing some examples so as to
provide a basic understanding of some aspects of the disclosure
without limiting or narrowing the scope or spirit of the disclosure
in any way. Other examples, aspects, and advantages will become
apparent from the following detailed description taken in
conjunction with the accompanying figures which illustrate the
principles of the described examples.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] For a more complete understanding of various examples,
reference is now made to the following detailed description taken
in connection with the accompanying drawings in which like
identifiers correspond to like elements:
[0012] FIG. 1A is a block diagram illustrating an example LiDAR
system according to embodiments of the present disclosure.
[0013] FIG. 1B is a block diagram illustrating an example of a
matched filtering module of a LiDAR system according to embodiments
of the present disclosure.
[0014] FIG. 2 is a time-frequency diagram illustrating an example
of FMCW LIDAR waveforms according to embodiments of the present
disclosure.
[0015] FIG. 3A is a diagram illustrating an example of a received
signal power spectrum density (PSD) in a LiDAR system, when the
scanning mirror has a low speed, according to embodiments of the
present disclosure.
[0016] FIG. 3B is a diagram illustrating an example of received
signal power spectrum density (PSD) in a LiDAR system, when the
scanning mirror has a high speed, according to embodiments of the
present disclosure.
[0017] FIG. 4 is a diagram illustrating an example of a matched
filter of a LiDAR system according to embodiments of the present
disclosure.
[0018] FIG. 5 is a diagram illustrating examples of matched filter
waveforms according to embodiments of the present disclosure.
[0019] FIG. 6 is a flow diagram illustrating an example of a
process of processing a received signal in a LiDAR system according
to embodiments of the present disclosure.
DETAILED DESCRIPTION
[0020] Various embodiments and aspects of the disclosures will be
described with reference to details discussed below, and the
accompanying drawings will illustrate the various embodiments. The
following description and drawings are illustrative of the
disclosure and are not to be construed as limiting the disclosure.
Numerous specific details are described to provide a thorough
understanding of various embodiments of the present disclosure.
However, in certain instances, well-known or conventional details
are not described in order to provide a concise discussion of
embodiments of the present disclosures.
[0021] The described LiDAR systems herein may be implemented in any
sensing market, such as, but not limited to, transportation,
manufacturing, metrology, medical, virtual reality, augmented
reality, and security systems. According to some embodiments, the
described LiDAR system may be implemented as part of a front-end of
frequency modulated continuous-wave (FMCW) device that assists with
spatial awareness for automated driver assist systems, or
self-driving vehicles.
[0022] FIG. 1A illustrates a LiDAR system 100 according to example
implementations of the present disclosure. The LiDAR system 100
includes one or more of each of a number of components, but may
include fewer or additional components than shown in FIG. 1.
According to some embodiments, one or more of the components
described herein with respect to LiDAR system 100 can be
implemented on a photonics chip. The optical circuits 101 may
include a combination of active optical components and passive
optical components. Active optical components may generate,
amplify, and/or detect optical signals and the like. In some
examples, the active optical component includes optical beams at
different wavelengths, and includes one or more optical amplifiers,
one or more optical detectors, or the like.
[0023] Free space optics 115 may include one or more optical
waveguides to carry optical signals, and route and manipulate
optical signals to appropriate input/output ports of the active
optical circuit. The free space optics 115 may also include one or
more optical components such as taps, wavelength division
multiplexers (WDM), splitters/combiners, polarization beam
splitters (PBS), collimators, couplers or the like. In some
examples, the free space optics 115 may include components to
transform the polarization state and direct received polarized
light to optical detectors using a PBS, for example. The free space
optics 115 may further include a diffractive element to deflect
optical beams having different frequencies at different angles.
[0024] In some examples, the LiDAR system 100 includes an optical
scanner 102 that includes one or more scanning mirrors that are
rotatable along an axis (e.g., a slow-moving-axis) that is
orthogonal or substantially orthogonal to the fast-moving-axis of
the diffractive element to steer optical signals to scan a target
environment according to a scanning pattern. For instance, the
scanning mirrors may be rotatable by one or more galvanometers.
Objects in the target environment may scatter an incident light
into a return optical beam or a target return signal. The optical
scanner 102 also collects the return optical beam or the target
return signal, which may be returned to the passive optical circuit
component of the optical circuits 101. For example, the return
optical beam may be directed to an optical detector by a
polarization beam splitter. In addition to the mirrors and
galvanometers, the optical scanner 102 may include components such
as a quarter-wave plate, lens, anti-reflective coating window or
the like.
[0025] To control and support the optical circuits 101 and optical
scanner 102, the LiDAR system 100 includes LiDAR control systems
110. The LiDAR control systems 110 may include a processing device
for the LiDAR system 100. In some examples, the processing device
may be one or more general-purpose processing devices such as a
microprocessor, central processing unit, or the like. More
particularly, the processing device may be complex instruction set
computing (CISC) microprocessor, reduced instruction set computer
(RISC) microprocessor, very long instruction word (VLIW)
microprocessor, or processor implementing other instruction sets,
or processors implementing a combination of instruction sets. The
processing device may also be one or more special-purpose
processing devices such as an application specific integrated
circuit (ASIC), a field programmable gate array (FPGA), a digital
signal processor (DSP), network processor, or the like.
[0026] In some examples, the LiDAR control systems 110 may include
a signal processing unit 112 such as a digital signal processor
(DSP). The LiDAR control systems 110 are configured to output
digital control signals to control optical drivers 103. In some
examples, the digital control signals may be converted to analog
signals through signal conversion unit 106. For example, the signal
conversion unit 106 may include a digital-to-analog converter. The
optical drivers 103 may then provide drive signals to active
optical components of optical circuits 101 to drive optical sources
such as lasers and amplifiers. In some examples, several optical
drivers 103 and signal conversion units 106 may be provided to
drive multiple optical sources.
[0027] The LiDAR control systems 110 are also configured to output
digital control signals for the optical scanner 102. A motion
control system 105 may control the galvanometers of the optical
scanner 102 based on control signals received from the LIDAR
control systems 110. For example, a digital-to-analog converter may
convert coordinate routing information from the LiDAR control
systems 110 to signals interpretable by the galvanometers in the
optical scanner 102. In some examples, a motion control system 105
may also return information to the LiDAR control systems 110 about
the position or operation of components of the optical scanner 102.
For example, an analog-to-digital converter may in turn convert
information about the galvanometers' position to a signal
interpretable by the LIDAR control systems 110.
[0028] The LiDAR control systems 110 are further configured to
analyze incoming digital signals. In this regard, the LiDAR system
100 includes optical receivers 104 to measure one or more beams
received by optical circuits 101. For example, a reference beam
receiver may measure the amplitude of a reference beam from the
active optical component, and an analog-to-digital converter
converts signals from the reference receiver to signals
interpretable by the LiDAR control systems 110. Target receivers
measure the optical signal that carries information about the range
and velocity of a target in the form of a beat frequency, modulated
optical signal. The reflected beam may be mixed with a second
signal from a local oscillator. The optical receivers 104 may
include a high-speed analog-to-digital converter to convert signals
from the target receiver to signals interpretable by the LiDAR
control systems 110. In some examples, the signals from the optical
receivers 104 may be subject to signal conditioning by signal
conditioning unit 107 prior to receipt by the LiDAR control systems
110. For example, the signals from the optical receivers 104 may be
provided to an operational amplifier for amplification of the
received signals and the amplified signals may be provided to the
LIDAR control systems 110.
[0029] In some applications, the LiDAR system 100 may additionally
include one or more imaging devices 108 configured to capture
images of the environment, a global positioning system 109
configured to provide a geographic location of the system, or other
sensor inputs. The LiDAR system 100 may also include an image
processing system 114. The image processing system 114 can be
configured to receive the images and geographic location, and send
the images and location or information related thereto to the LiDAR
control systems 110 or other systems connected to the LIDAR system
100.
[0030] In operation according to some examples, the LiDAR system
100 is configured to use nondegenerate optical sources to
simultaneously measure range and velocity across two dimensions.
This capability allows for real-time, long range measurements of
range, velocity, azimuth, and elevation of the surrounding
environment.
[0031] In some examples, the scanning process begins with the
optical drivers 103 and LiDAR control systems 110. The LiDAR
control systems 110 instruct the optical drivers 103 to
independently modulate one or more optical beams, and these
modulated signals propagate through the passive optical circuit to
the collimator. The collimator directs the light at the optical
scanning system that scans the environment over a preprogrammed
pattern defined by the motion control system 105. The optical
circuits 101 may also include a polarization wave plate (PWP) to
transform the polarization of the light as it leaves the optical
circuits 101. In some examples, the polarization wave plate may be
a quarter-wave plate or a half-wave plate. A portion of the
polarized light may also be reflected back to the optical circuits
101. For example, lensing or collimating systems used in LIDAR
system 100 may have natural reflective properties or a reflective
coating to reflect a portion of the light back to the optical
circuits 101.
[0032] Optical signals reflected back from the environment pass
through the optical circuits 101 to the receivers. Because the
polarization of the light has been transformed, it may be reflected
by a polarization beam splitter along with the portion of polarized
light that was reflected back to the optical circuits 101.
Accordingly, rather than returning to the same fiber or waveguide
as an optical source, the reflected light is reflected to separate
optical receivers. These signals interfere with one another and
generate a combined signal. Each beam signal that returns from the
target produces a time-shifted waveform. The temporal phase
difference between the two waveforms generates a beat frequency
measured on the optical receivers (photodetectors). The combined
signal can then be reflected to the optical receivers 104.
[0033] The analog signals from the optical receivers 104 are
converted to digital signals using ADCs. The digital signals are
then sent to the LiDAR control systems 110. A signal processing
unit 112 may then receive the digital signals and interpret them.
In some embodiments, the signal processing unit 112 also receives
position data from the motion control system 105 and galvanometers
(not shown) as well as image data from the image processing system
114. The signal processing unit 112 can then generate a 3D point
cloud with information about range and velocity of points in the
environment as the optical scanner 102 scans additional points. The
signal processing unit 112 can also overlay a 3D point cloud data
with the image data to determine velocity and distance of objects
in the surrounding area. The system also processes the
satellite-based navigation location data to provide a precise
global location.
[0034] FIG. 1B is a block diagram illustrating an example of a
matched filtering module 130 of a LiDAR system according to
embodiments of the present disclosure. Referring to FIG. 1A and
FIG. 1B, the signal processing unit 112 may include the matched
filtering module 130. It should be noted that, although the matched
filtering module is depicted as residing within the signal
processing unit 112, embodiments of the present disclosure are not
limited as such. For instance, in one embodiment, the matched
filtering module 130 can reside in computer memory (e.g., RAM, ROM,
flash memory, and the like) within system 100 (e.g., LiDAR control
system 110). The scanning FMCW LiDAR system 100 may use a moving
scanning mirror (e.g., included in optical scanner 102) to steer
light beams and scan a target or a target environment. Objects in
the target environment may scatter an incident light into a return
optical beam or a target return signal. The optical scanner 102
also collects the return optical beam or the target return signal.
The target return signal may be mixed with a second signal from a
local oscillator, and a range dependent beat frequency may be
generated. The temporal phase difference between the two waveforms
may generate the beat frequency measured on the optical receivers
104 (photodetectors). In one embodiment, the beat frequency may be
digitized by an analog-to-digital converter (ADC), for example, in
a signal conditioning unit such as signal conditioning unit 107 in
LiDAR system 100. In one embodiment, the digitized beat frequency
signal may be received by the signal processing unit 112 LiDAR
system 100, and then be digitally processed in the signal
processing unit 112. The signal processing unit 112 including the
matched filtering module 130 may process the received signal to
extract range and velocity information of the target.
[0035] The matched filtering module 130 may include, but not being
limited to, a sampling module 121, a conversion module 122, a
selection module 123, a coefficient module 124, and a filtering
module 125. In some embodiments, the matched filtering module 130
may receive a signal from the optical receivers 104 or the signal
conditioning unit 107. The sampling module 121 may be configured to
sample the received signal at the LiDAR system. The conversion
module 122 may be configured to convert the received signal to a
frequency domain, where the received signal includes a first
frequency waveform. The selection unit 123 may be configured to
select a matched filter, where the matched filter may include a
second frequency waveform with a set of coefficients to match the
first frequency waveform. The second frequency waveform may include
an expected first frequency waveform of the received signal. For
example, the received signal may be the beat frequency generated
from the mixing of the target return signal and the local
oscillator signal, thus, the second frequency waveform may be
determined based on a simulation (a model) or a measurement result
of the received signal. The coefficient unit 124 may be configured
to update the set of coefficients according to a set of metrics.
The filtering unit 125 may be configured to filter the received
signal by the matched filter to generate a filtered received
signal. The signal processing unit may be configured to extract
range and velocity information of the target from the filtered
received signal. The matched filtering module 130 may include other
modules. Some or all of modules 121-125 may be implemented in
software, hardware, or a combination thereof. For example, these
modules may be loaded into a memory, and executed by one or more
processors. Some of modules 121-125 may be integrated together as
an integrated module.
[0036] FIG. 2 is a time-frequency diagram 200 of an FMCW scanning
signal 101b that can be used by a LiDAR system, such as system 100,
to scan a target environment according to some embodiments. In one
example, the scanning waveform 201, labeled as f.sub.FM(t), is a
sawtooth waveform (sawtooth "chirp") with a chirp bandwidth
.DELTA.f.sub.C and a chirp period T.sub.C. The slope of the
sawtooth is given as k=(.DELTA.f.sub.C/T.sub.C). FIG. 2 also
depicts target return signal 202 according to some embodiments.
Target return signal 202, labeled as f.sub.FM(t-.DELTA.t), is a
time-delayed version of the scanning signal 201, where .DELTA.t is
the round trip time to and from a target illuminated by scanning
signal 201. The round trip time is given as .DELTA.t=2R/v, where R
is the target range and v is the velocity of the optical beam,
which is the speed of light c. The target range, R, can therefore
be calculated as R=c(.DELTA.t/2). When the return signal 202 is
optically mixed with the scanning signal, a range dependent
difference frequency ("beat frequency") .DELTA.f.sub.R(t) is
generated. The beat frequency .DELTA.f.sub.R(t) is linearly related
to the time delay .DELTA.t by the slope of the sawtooth k. That is,
.DELTA.f.sub.R(t)=k.DELTA.t. Since the target range R is
proportional to .DELTA.t, the target range R can be calculated as
R=(c/2)(.DELTA.f.sub.R(t)/k). That is, the range R is linearly
related to the beat frequency .DELTA.f.sub.R(t). The beat frequency
.DELTA.f.sub.R(t) can be generated, for example, as an analog
signal in optical receivers 104 of system 100. The beat frequency
can then be digitized by an analog-to-digital converter (ADC), for
example, in a signal conditioning unit such as signal conditioning
unit 107 in LIDAR system 100. The digitized beat frequency signal
can then be digitally processed, for example, in a signal
processing unit, such as signal processing unit 112 in system 100.
It should be noted that the target return signal 202 will, in
general, also includes a frequency offset (Doppler shift) if the
target has a velocity relative to the LIDAR system 100. The Doppler
shift can be determined separately, and used to correct the
frequency of the return signal, so the Doppler shift is not shown
in FIG. 2 for simplicity and ease of explanation. It should also be
noted that the sampling frequency of the ADC will determine the
highest beat frequency that can be processed by the system without
aliasing. In general, the highest frequency that can be processed
is one-half of the sampling frequency (i.e., the "Nyquist limit").
In one example, and without limitation, if the sampling frequency
of the ADC is 1 gigahertz, then the highest beat frequency that can
be processed without aliasing (.DELTA.f.sub.Rmax) is 500 megahertz.
This limit in turn determines the maximum range of the system as
R.sub.max=(c/2)(.DELTA.f.sub.Rmax/k) which can be adjusted by
changing the chirp slope k. In one example, while the data samples
from the ADC may be continuous, the subsequent digital processing
described below may be partitioned into "time segments" that can be
associated with some periodicity in the LIDAR system 100. In one
example, and without limitation, a time segment might correspond to
a predetermined number of chirp periods T, or a number of full
rotations in azimuth by the optical scanner.
[0037] FIG. 3A is a diagram 300a illustrating an example of
received signal power spectrum density (PSD) 301a in a LiDAR
system, when the scanning mirror has a low speed. FIG. 3B is a
diagram illustrating an example of received signal power spectrum
density (PSD) in a LiDAR system, when the scanning mirror has a
high speed. A scanning LiDAR system (e.g., FMCW LiDAR) may use a
moving scanning mirror to steer light beams and scan a target or a
target environment. To achieve a wide field of view and high frame
rates, the scanning mirror may have a high angular speed. In some
scenarios, the high mirror angular speed may cause several
impairments. For example, the mirror-induced Doppler shift may
broaden the received signal bandwidth. As such, in these scenarios,
the received signal intensity may be lowered, and consequently the
detection probability may be reduced and cause an increase in
errors related to range, velocity, and reflectivity
measurements.
[0038] Referring to FIG. 3A and FIG. 3B, the moving scanning mirror
(e.g., scanning mirror included as part of system 100 in FIG. 1)
may induce Doppler Shift on the outgoing light beam and the
incoming light beam, which may be the target return signal. As
depicted in FIG. 3A, when the scanning mirror is moving at a low
mirror speeds (e.g., <5 kdeg/s), the mirror-induced Doppler has
little impact on the signal quality. The peak value 302a may be
detected in the PSD 301a of the received signal. The received
signal may have random realization 305a, which may be minor. The
received signal may have a reasonable range of frequency
measurement error 303a and a reasonable range of power measurement
error 304a.
[0039] As depicted in FIG. 3B, when the scanning mirror is moving
at a high mirror speeds (>5 kdeg/s), there may be a significant
broadening of the signal power spectrum density (PSD) 301b. As a
result, the measured signal energy may be lower on average. Thus,
the probability of detection may be consequently reduced. The
measurement error on frequency 303b and/or the measurement error on
energy 304b may be higher due to the randomness (e.g., random
realization 305b) of the signal.
[0040] FIG. 4 is a diagram 400 illustrating an example of a matched
filter of a LiDAR system according to embodiments of the present
disclosure. The embodiments described herein provide multiple
approaches to combat mirror Doppler spreading. For example,
frequency domain techniques and time domain techniques can be
employed by embodiments. One approach in the frequency domain
techniques is matched filtering in the frequency domain. Under this
approach, the received signal is filtered by a matched filter in
the frequency domain, where the matched filter includes an expected
received signal shape or waveform in frequency domain. The expected
received signal frequency waveform may be determined based on
theoretical models or simulations or measurements from
predetermined conditions (e.g., conditions determined in a lab
setting or testing environment, artificial intelligence, . . . ,
etc.).
[0041] Referring to FIG. 4, a received signal 401 in the frequency
domain, for example, an input spectrum, may be input into the
matched filter 402. The received signal 401 may include a first
frequency waveform, which may be an unknown waveform, e.g., at a
starting point of a matched filtering process. The matched filter
402 may include a second frequency waveform, which may be the
expected received signal frequency waveform. In some embodiments,
the second frequency waveform may have a set of coefficients to
match or approximate the first frequency waveform. In some
embodiments, the second frequency waveform may be an expectation or
an estimation or an approximation of the first frequency waveform,
determined based on a theoretical model or experimental
measurements. In some embodiments, the second frequency waveform
may be determined based on a model or a simulation or a measurement
of the LiDAR system, e.g., an optical-subsystem of the LiDAR
system.
[0042] In one embodiment, the second frequency waveform may be
based on an estimation of a power spectrum density (PSD) function
of the received signal. For example, the matched filter 402 may
include an expected received signal PSD. The matched filter 402 may
be configured to compare the expected received signal PSD to the
first frequency waveform and determine if there is a match.
[0043] In one embodiment, the filter coefficients 403 for the
matched filter 402 may be constant. For example, the filter
coefficients 403 may be derived from a theoretical simulation or
modeling.
[0044] In one embodiment, the filter coefficients 403 may be
updated according to a set of metrics. For example, the filter
coefficients 403 may be updated depending on key factors such as an
angular speed of a scanning mirror, a position of the scanning
mirror, an optical scanner geometry, a scanning mirror size, a beam
diameter, or a target, etc. The set of metrics may include the
angular speed of the scanning mirror, the position of the scanning
mirror, the optical scanner geometry, the scanning mirror size, the
beam diameter, or a target, etc. For example, the filter
coefficients 403 may be adapted or adjusted to better match the
received signal. For example, the filter coefficients 403 may be
initially determined from the theoretical simulation or modeling,
then dynamically updated or adapted based on the angular speed of
the scanning mirror, the position of the scanning mirror, the
optical scanner geometry, or the target, etc. For example, when the
angular speed of the scanning mirror is faster, the filter
coefficients 403 may be updated to broaden a bandwidth of the
matched filter.
[0045] In one embodiment, the matched filter coefficients 403 may
be updated such that the matched filter bandwidth is proportional
to the angular speed of the scanning mirror, the scanning mirror
size, and/or the beam diameter.
[0046] In one embodiment, the set of coefficients may be updated
based on a change in a hardware configuration or a system
operation. For example, the set of coefficients may be updated
based on an increase of a mirror angular speed or a change of a
scan pattern.
[0047] In one embodiment, the filter coefficients 403 may be
updated continuously, e.g., updated per 1 millisecond, 1 second, 15
seconds, 30 seconds, or any values therebetween. For another
example, the filter coefficients 403 may be updated when detecting
there is a change in the angular speed of the scanning mirror, the
position of the scanning mirror, the optical scanner geometry, or
the target, etc.
[0048] According to some embodiments, the matched filter 402 may be
configured based on convolving waveforms. For example, in one
scenario, the matched filter 402 may be configured to compare the
received signal (e.g., the first frequency waveform) with the
expected received signal (e.g., the second frequency waveform) to
determine a similarity between them. As an example, the matched
filter 402 may be configured to calculate a cross-correlation of
the received signal PSD with the expected received signal PSD. For
example, the maximum correlation value may represent the peak value
of the received signal.
[0049] If the second frequency waveform which is the filtering
known waveform is the complex conjugate of the received signal
waveform which is the unknown waveform, then the signal-to-noise
ratio (SNR) and probability of detection will be maximized by the
matched filter 402. In one embodiment, the filtered received signal
is inputted into a peak selection process to extract the range and
velocity information. A peak value search 404 may be performed to
detect a peak value from the received signal. Then, range and
velocity information of the target may be extracted based on the
peak value in the received signal. As the detection 405 occurs at
the point where SNR is maximized, the method may result in more
accurate frequency and energy measurements, thereby increasing the
accuracy in range and velocity measurements of the target.
[0050] FIG. 5 is a diagram illustrating examples of matched filter
waveforms according to embodiments of the present disclosure.
Different matched filter waveforms (e.g., the second frequency
waveform) may be selected based on theoretical simulation or
modeling, or may be selected empirically. As an example, a matched
filter may include a Gaussian waveform 501, where M(f)=exp
(-0.5(f/B).sup.2), where B determines a filter bandwidth. As
another example, a matched filter may include a sinc waveform 502,
where M(f)=sinc(f/B), if |f|.ltoreq.B; M(f)=0, otherwise, where B
determines a filter bandwidth. As yet another example, a matched
filter may include a sinc squared waveform 503, where
M(f)=sinc.sup.2(f/B), if |f|.ltoreq.B; M(f)=0, otherwise, where B
determines a filter bandwidth. As still another example, a matched
filter may include a rectangular waveform 501, where M(f)=1, if
|f|.ltoreq.B; M(f)=0, otherwise, where B determines a filter
bandwidth. The above examples of the match filters may be defined
by the parameter B, which determines the filter bandwidth. In one
embodiment, the filter bandwidth may be directly proportional to
the angular speed of the scanning mirror. The above examples of the
match filters are only for illustration. There may be many other
matched filter waveforms.
[0051] For implementation in digital signal processing,
discrete-frequency filter coefficients may be obtained by sampling
the continuous-frequency waveforms (e.g., 501-504).
[0052] FIG. 6 is a flow diagram illustrating an example of a
process 600 of processing a received signal in a LiDAR system
according to embodiments of the present disclosure. Process 600 may
be performed by processing logic which may include software,
hardware, or a combination thereof. The software may be stored on a
non-transitory machine readable storage medium (e.g., on a memory
device). For example, the process 600 may be performed by the
matched filtering module 130 in the signal processing unit 112 of
the LiDAR system 100, as illustrated in FIG. 1A-FIG. 1B. By this
process, more accurate frequency and energy measurements may be
achieved, thereby increasing the accuracy in range and velocity
measurements of the target.
[0053] At block 601, the received signal is sampled at the LiDAR
system and the received signal is converted to a frequency domain,
where the received signal comprises a first frequency waveform.
[0054] At block 602, a matched filter is selected. The matched
filter comprises a second frequency waveform with a set of
coefficients to match the first frequency waveform. In one
embodiment, the second frequency waveform is determined based on a
model or a simulation or a measurement of an optical sub-system of
the LiDAR system. In one embodiment, the second frequency waveform
is determined based on an estimation of a PSD of the received
signal.
[0055] In one embodiment, selecting a matched filter comprises
selecting a rectangular waveform, a sinc waveform, a sinc squared
waveform, or a Gaussian waveform to be the second frequency
waveform. In one embodiment, the matched filter comprises at least
one of a sinc waveform, a sinc squared waveform, a Gaussian
waveform, or a rectangular waveform.
[0056] At block 603, the set of coefficients are updated according
to a set of metrics. In one embodiment, the set of coefficients of
the matched filter are updated according to at least one of an
angular speed of a scanning mirror, a position of the scanning
mirror, a geometry of an optical scanner, or the target.
[0057] In one embodiment, the set of coefficients are updated such
that a filter bandwidth is proportional to an angular speed of a
scanning mirror, a scanning mirror size, or a beam diameter. In one
embodiment, the set of coefficients are updated based on a change
in a hardware configuration or a system operation including a
change of a mirror angular speed or a scan pattern change.
[0058] At block 604, the received signal may be filtered by the
matched filter to generate a filtered received signal.
[0059] At block 605, range and velocity information is extracted
from the filtered received signal. In one embodiment, the filtered
received signal is inputted into a peak selection process to
extract the range and velocity information. For example, a peak
value of the filtered received signal is detected to extract range
and velocity information of the target.
[0060] The preceding description sets forth numerous specific
details such as examples of specific systems, components, methods,
and so forth, in order to provide a thorough understanding of
several examples in the present disclosure. It will be apparent to
one skilled in the art, however, that at least some examples of the
present disclosure may be practiced without these specific details.
In other instances, well-known components or methods are not
described in detail or are presented in simple block diagram form
in order to avoid unnecessarily obscuring the present disclosure.
Thus, the specific details set forth are merely exemplary.
Particular examples may vary from these exemplary details and still
be contemplated to be within the scope of the present
disclosure.
[0061] Any reference throughout this specification to "one example"
or "an example" means that a particular feature, structure, or
characteristic described in connection with the examples are
included in at least one example. Therefore, the appearances of the
phrase "in one example" or "in an example" in various places
throughout this specification are not necessarily all referring to
the same example.
[0062] Although the operations of the methods herein are shown and
described in a particular order, the order of the operations of
each method may be altered so that certain operations may be
performed in an inverse order or so that certain operation may be
performed, at least in part, concurrently with other operations.
Instructions or sub-operations of distinct operations may be
performed in an intermittent or alternating manner.
[0063] The above description of illustrated implementations of the
invention, including what is described in the Abstract, is not
intended to be exhaustive or to limit the invention to the precise
forms disclosed. While specific implementations of, and examples
for, the invention are described herein for illustrative purposes,
various equivalent modifications are possible within the scope of
the invention, as those skilled in the relevant art will recognize.
The words "example" or "exemplary" are used herein to mean serving
as an example, instance, or illustration. Any aspect or design
described herein as "example" or "exemplary" is not necessarily to
be construed as preferred or advantageous over other aspects or
designs. Rather, use of the words "example" or "exemplary" is
intended to present concepts in a concrete fashion. As used in this
application, the term "or" is intended to mean an inclusive "or"
rather than an exclusive "or". That is, unless specified otherwise,
or clear from context, "X includes A or B" is intended to mean any
of the natural inclusive permutations. That is, if X includes A; X
includes B; or X includes both A and B, then "X includes A or B" is
satisfied under any of the foregoing instances. In addition, the
articles "a" and "an" as used in this application and the appended
claims should generally be construed to mean "one or more" unless
specified otherwise or clear from context to be directed to a
singular form. Furthermore, the terms "first," "second," "third,"
"fourth," etc. as used herein are meant as labels to distinguish
among different elements and may not necessarily have an ordinal
meaning according to their numerical designation.
* * * * *