Techniques for compensating for mirror doppler spread in a coherent lidar system using matched filtering

Abstract

The received signal is sampled at the LiDAR system and converted to the frequency domain, wherein the received signal includes a first frequency waveform. A matched filter is selected, wherein the matched filter includes a second frequency waveform having a set of coefficients used to match the first frequency waveform. The set of coefficients is 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 are extracted from the filtered received signal.

Description

[0001] Related applications

[0002] This application claims the benefit of U.S. Patent Application 17 / 354,324, filed June 22, 2021, pursuant to 35 U.S. SC §119(e), which claims priority to U.S. Provisional Patent Application 63 / 093,599, filed October 19, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to optical detection and ranging (LiDAR) systems, for example, techniques for compensating for mirror Doppler spread in coherent LiDAR systems. Background Technology

[0004] Frequency-modulated continuous wave (FMCW) LiDAR systems involve several potential phase impairments, such as laser phase noise, circuit phase noise, scintillation noise injected into the laser by driving electronics, temperature / weather drift, and chirp rate shift. Scanning FMCW LiDAR systems use a moving scanning mirror to guide the beam and scan the target or target environment. To achieve a wide field of view and high frame rate, the scanning mirror can have a high angular rate. A high mirror angular rate can lead to several impairments. For example, mirror-induced Doppler shift can widen the received signal bandwidth. The received signal strength may decrease, and therefore the detection probability may decrease. Consequently, errors in range, velocity, and reflectivity measurements may increase. Summary of the Invention

[0005] This disclosure describes various examples, but is not limited to, methods for processing received signals in LiDAR systems.

[0006] In some examples, this paper discloses a method for processing a received signal with a matched filter, for example, to compensate for mirror Doppler spread. The matched filter filters the received signal based on the expected shape or waveform of the received signal. For example, the received signal in the frequency domain (or “input spectrum”) can be filtered by a matched filter designed to match the expected received signal power spectral density (PSD). The filter coefficients can be constant (e.g., derived from theoretical simulation or modeling) or updated depending on key factors such as mirror angular rate, mirror position, scanner geometry, target, scene, etc. Because detection occurs at the point where the SNR is maximized, this method can yield more accurate frequency and energy measurements.

[0007] In some examples, this document discloses a method in a LiDAR system. A received signal is sampled at the LiDAR system and converted to the frequency domain, wherein the received signal includes a first frequency waveform. A matched filter is selected, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform. The set of coefficients is updated according to a set of metrics. The received signal is filtered using the matched filter to generate a filtered received signal. Range and velocity information are extracted from the filtered received signal.

[0008] In some examples, this document discloses a LiDAR system. The LiDAR system includes a memory and a processing means or processor operatively coupled to the memory. The processing means or processor is configured to sample a received signal at the LiDAR system and convert the received signal to the frequency domain, wherein the received signal includes a first frequency waveform. The processing means or processor is further configured to select a matched filter, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform. The processing means or processor is further configured to update the set of coefficients according to a set of metrics and filter the received signal using the matched filter to generate a filtered received signal. The processing means or processor is further configured to extract range and velocity information from the filtered received signal.

[0009] In some examples, this document discloses a non-transitory machine-readable medium. Instructions are stored in the non-transitory machine-readable medium that, 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 the frequency domain, wherein the received signal includes a first frequency waveform. The processing device or processor is further configured to select a matched filter, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform. The processing device or processor is further configured to update the set of coefficients according to a set of metrics and filter the received signal using the matched filter to generate a filtered received signal. The processing device or processor is further configured to extract range and velocity information from the filtered received signal.

[0010] These and other aspects of this disclosure will become apparent from the following detailed description together with the accompanying drawings, which are briefly described below. This disclosure includes any combination of two, three, four, or more than four features or elements set forth in this disclosure, regardless of whether such features or elements are explicitly combined or otherwise described in the particular example implementation described herein. This disclosure is intended to be read holistically, such that any separable feature or element of this disclosure shall be considered composable in any aspect and example of this disclosure unless the context of this disclosure expressly specifies otherwise.

[0011] Therefore, it will be understood that this summary is provided merely for the purpose of outlining some examples to provide a basic understanding of some aspects of this disclosure, and not in any way to limit or narrow the scope or spirit of this disclosure. Other examples, aspects, and advantages will become apparent from the following detailed description taken in conjunction with the accompanying drawings, which illustrate the principles of the described examples. Attached Figure Description

[0012] To gain a more complete understanding of the various examples, please now refer to the following detailed description taken in conjunction with the accompanying drawings, in which the same reference numerals correspond to the same elements:

[0013] Figure 1A This is a block diagram illustrating an example LiDAR system according to an embodiment of the present disclosure.

[0014] Figure 1B This is a block diagram illustrating an example of a matched filtering module of a LiDAR system according to an embodiment of the present disclosure.

[0015] Figure 2 This is a time-frequency diagram illustrating an example of an FMCW LiDAR waveform according to an embodiment of the present disclosure.

[0016] Figure 3A This is a diagram illustrating an example of the received signal power spectral density (PSD) in a LiDAR system when the scanning mirror has a low rate, according to an embodiment of the present disclosure.

[0017] Figure 3B This is a diagram illustrating an example of the received signal power spectral density (PSD) in a LiDAR system when the scanning mirror has a high rate, according to an embodiment of the present disclosure.

[0018] Figure 4 This is a diagram illustrating an example of a matched filter in a LiDAR system according to an embodiment of the present disclosure.

[0019] Figure 5 This is a diagram illustrating an example of a matched filter waveform according to an embodiment of the present disclosure.

[0020] Figure 6This is a flowchart illustrating an example of processing a received signal in a LiDAR system according to an embodiment of the present disclosure. Detailed Implementation

[0021] Various embodiments and aspects of this disclosure will be described with reference to the details of the following discussion, and the accompanying drawings will illustrate various embodiments. The following description and drawings are illustrative of this disclosure and should not be construed as limiting it. Numerous specific details are described to provide a thorough understanding of various embodiments of this disclosure. However, in some instances, well-known or conventional details have not been described to provide a brief discussion of embodiments of this disclosure.

[0022] The LiDAR system described herein can 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 can be implemented as part of the front end of a frequency modulated continuous wave (FMCW) device that assists automated driver assistance systems or autonomous vehicles in spatial perception.

[0023] Figure 1A An example of a LiDAR system 100 implemented according to this disclosure is illustrated. The LiDAR system 100 includes one or more of a plurality of components, but may include more than one of a plurality of components. Figure 1A The components shown are fewer or additional. According to some embodiments, one or more of the components depicted herein with respect to the LiDAR system 100 may be implemented on a photonic chip. The optical circuitry 101 may include a combination of active and passive optical components. Active optical components may generate, amplify, and / or detect optical signals, etc. In some examples, active optical components include light beams of different wavelengths and include one or more optical amplifiers, one or more optical detectors, etc.

[0024] Free-space optics 115 may include one or more optical waveguides to carry optical signals and route and manipulate them to appropriate input / output ports of active optical circuitry. Free-space optics 115 may also include one or more optical components, such as taps, wavelength division multiplexers (WDMs), beam splitters / combiners, polarization beam splitters (PBSs), collimators, couplers, etc. In some examples, free-space optics 115 may include components for transforming polarization states and guiding received polarized light to an optical detector using a PBS. Free-space optics 115 may also include diffraction elements that deflect beams of different frequencies at different angles.

[0025] In some examples, the LiDAR system 100 includes an optical scanner 102 comprising one or more scanning mirrors rotatable along an axis orthogonal or substantially orthogonal to the fast-moving axis of the diffraction element (e.g., a slow-moving axis) to guide optical signals to scan a target environment according to a scanning pattern. For example, the scanning mirrors may be rotatable via one or more galvanometers. Objects in the target environment may scatter incident light into an echo beam or target echo signal. The optical scanner 102 also collects the echo beam or target echo signal, which may be returned to passive optical circuitry components of the optical circuitry 101. For example, the echo beam may be guided to an optical detector via a polarizing beam splitter. In addition to the mirrors and galvanometers, the optical scanner 102 may also include components such as quarter-wave plates, lenses, and anti-reflective coated windows.

[0026] To control and support the optical circuitry 101 and the optical scanner 102, the LiDAR system 100 includes a LiDAR control system 110. The LiDAR control system 110 may include processing means for the LiDAR system 100. In some examples, the processing means may be one or more general-purpose processing means, such as a microprocessor, a central processing unit, etc. More specifically, the processing means may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computer (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets, or a processor implementing combinations of instruction sets. The processing means may also be one or more special-purpose processing means, such as an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a network processor, etc.

[0027] In some examples, the LiDAR control system 110 may include a signal processing unit 112, such as a digital signal processor (DSP). The LiDAR control system 110 is configured to output digital control signals to control the optical driver 103. In some examples, the digital control signals may be converted into analog signals by a signal conversion unit 106. For example, the signal conversion unit 106 may include a digital-to-analog converter. The optical driver 103 can then provide drive signals to the active optical components of the optical circuit 101 to drive light sources such as lasers and amplifiers. In some examples, several optical drivers 103 and signal conversion units 106 may be provided to drive multiple light sources.

[0028] The LiDAR control system 110 is also configured to output digital control signals for the optical scanner 102. The motion control system 105 can control the galvanometer of the optical scanner 102 based on the control signals received from the LiDAR control system 110. For example, a digital-to-analog converter (DAC) can convert coordinate routing information from the LiDAR control system 110 into signals that can be interpreted by the galvanometer in the optical scanner 102. In some examples, the motion control system 105 can also return information to the LiDAR control system 110 relating to the position or operation of components of the optical scanner 102. For example, an DAC can further convert information about the galvanometer position into signals that can be interpreted by the LiDAR control system 110.

[0029] The LiDAR control system 110 is also configured to analyze incoming digital signals. In this regard, the LiDAR system 100 includes an optical receiver 104 for measuring one or more beams received by the optical circuitry 101. For example, a reference beam receiver may measure the amplitude of a reference beam from active optics, and an analog-to-digital converter converts the signal from the reference receiver into a signal interpretable by the LiDAR control system 110. A target receiver measures an optical signal carrying information related to the range and velocity of the target, in the form of a beat-modulated optical signal. The reflected beam may be mixed with a second signal from a local oscillator. The optical receiver 104 may include a high-speed analog-to-digital converter to convert the signal from the target receiver into a signal interpretable by the LiDAR control system 110. In some examples, the signal from the optical receiver 104 may be signal-conditioned by a signal conditioning unit 107 before being received by the LiDAR control system 110. For example, the signal from the optical receiver 104 may be provided to an operational amplifier to amplify the received signal, and the amplified signal may be provided to the LiDAR control system 110.

[0030] 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 the system's geographic location, or other sensor inputs. The LiDAR system 100 may also include an image processing system 114. The image processing system 114 may be configured to receive images and geographic locations, and to transmit the images and locations, or related information, to the LiDAR control system 110 or other systems connected to the LiDAR system 100.

[0031] In some example operations, the LiDAR system 100 is configured to use a non-degenerate light source to simultaneously measure range and velocity across two dimensions. This capability allows for real-time, remote measurement of the range, velocity, azimuth, and elevation of the surrounding environment.

[0032] In some examples, the scanning process begins with an optical driver 103 and a LiDAR control system 110. The LiDAR control system 110 instructs the optical driver 103 to independently modulate one or more beams, and these modulated signals are propagated through passive optical circuitry to a collimator. The collimator guides the light at an optical scanning system used to scan the environment on a pre-programmed pattern defined by a motion control system 105. The optical circuitry 101 may also include a polarizing waveplate (PWP) to change the polarization of the light as it leaves the optical circuitry 101. In some examples, the polarizing waveplate 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 circuitry 101. For example, the lens or collimation system used in the LiDAR system 100 may have natural reflective properties or a reflective coating to reflect a portion of the light back to the optical circuitry 101.

[0033] The optical signal reflected from the environment is transmitted to the receiver via optical circuit 101. Since the polarization of the light has been transformed, it can be reflected together with a portion of the polarized light reflected back to optical circuit 101 by a polarization beamsplitter. Therefore, the reflected light does not return to the same fiber or waveguide as the light source, but is reflected to a separate optical receiver. These signals interfere with each other and generate a combined signal. The individual beams of signal returning from the target produce time-shifted waveforms. The time phase difference between the two waveforms generates a beat frequency measured on the optical receiver (photodetector). The combined signal can then be reflected back to optical receiver 104.

[0034] The analog signal from the optical receiver 104 is converted into a digital signal using an ADC. The digital signal is then sent to the LiDAR control system 110. The signal processing unit 112 can then receive and interpret the digital signals. In some embodiments, the signal processing unit 112 also receives position data from the motion control system 105 and a galvanometer (not shown), as well as image data from the image processing system 114. Then, as the optical scanner 102 scans additional points, the signal processing unit 112 can utilize information related to the range and velocity of points in the environment to generate a 3D point cloud. The signal processing unit 112 can also overlay the 3D point cloud data with the image data to determine the velocity and distance of objects in the surrounding area. The system also processes satellite-based navigation and positioning data to provide accurate global positioning.

[0035] Figure 1B This is a block diagram illustrating an example of a matched filtering module 130 of a LiDAR system according to an embodiment of the present disclosure. Reference Figure 1A and Figure 1BThe signal processing unit 112 may include a 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 this disclosure are not limited thereto. For example, in one embodiment, the matched filtering module 130 may reside in a computer memory (e.g., RAM, ROM, flash memory, etc.) within the system 100 (e.g., a LiDAR control system 110). The scanning FMCW LiDAR system 100 may use (e.g., included in the optical scanner 102) a moving scanning mirror to guide the beam and scan the target or target environment. Objects in the target environment may scatter the incident light into an echo beam or target echo signal. The optical scanner 102 also collects the echo beam or target echo signal. The target echo signal may be mixed with a second signal from a local oscillator, and a range-dependent beat frequency may be generated. The time phase difference between the two waveforms may generate a beat frequency measured on the optical receiver 104 (photodetector). In one embodiment, the beat frequency can be digitized by an analog-to-digital converter (ADC) in a signal conditioning unit (such as signal conditioning unit 107 in LiDAR system 100). In one embodiment, the digitized beat frequency signal can be received by signal processing unit 112 in LiDAR system 100 and then digitally processed in signal processing unit 112. Signal processing unit 112, including matched filtering module 130, can process the received signal to extract range and velocity information of the target.

[0036] Matched filtering module 130 may include, but is not limited to, sampling module 121, conversion module 122, selection module 123, coefficient module 124, and filtering module 125. In some embodiments, matched filtering module 130 may receive signals from optical receiver 104 or signal conditioning unit 107. Sampling module 121 may be configured to sample the received signal at the LiDAR system. Conversion module 122 may be configured to convert the received signal to the frequency domain, wherein the received signal includes a first frequency waveform. Selection unit 123 may be configured to select a matched filter, wherein the matched filter may include a second frequency waveform having a set of coefficients for matching the first frequency waveform. The second frequency waveform may include the expected first frequency waveform of the received signal. For example, the received signal may be a beat frequency generated based on a mixture of a target echo signal and a local oscillator signal, and thus the second frequency waveform may be determined based on an analog (model) or measurement of the received signal. Coefficient unit 124 may be configured to update the coefficient set according to a set of metrics. Filtering unit 125 may be configured to filter the received signal through the matched filter to generate a filtered received signal. The signal processing unit can be configured to extract the target's range and velocity information from the filtered received signal. The matched filtering module 130 may include other modules. Some or all of modules 121 to 125 can be implemented in software, hardware, or a combination thereof. For example, these modules can be loaded into memory and executed by one or more processors. Some modules 121 to 125 can be integrated together as an integrated module.

[0037] Figure 2 This is a time-frequency diagram 200 of an FMCW scan 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, it is labeled f FM The scan waveform 201 of (t) has a chirped bandwidth Δf C The sawtooth waveform with chirp period TC (sawtooth "chirp"). The slope of the sawtooth is given as k = (Δf C / T C ). Figure 2 The target echo signal 202 according to some embodiments is also depicted. (Labeled as f) FM The target echo signal 202 (t-Δt) is a delayed version of the scanning signal 201, where Δt is the round-trip time relative to the target illuminated by the scanning signal 201. The round-trip time is given as Δt = 2R / v, where R is the target range and v is the beam velocity, i.e., the speed of light c. Therefore, the target range R can be calculated as R = c(Δt / 2). When the echo signal 202 is optically mixed with the scanning signal, a range-dependent difference frequency (“beat frequency”) Δf is generated. R (t). The beat frequency Δf is obtained through the slope k of the sawtooth.R (t) is linearly related to the time delay Δt. That is, Δf R (t) = kΔt. Since the target range R is proportional to Δt, the target range R can be calculated as R = (c / 2)(Δf) / (c / 2)(Δt / 2)(c ... R (t) / k). That is, the range R and the beat frequency Δf R (t) Linear correlation. Beat frequency Δf R (t) can be generated as an analog signal, for example, in the optical receiver 104 of system 100. Then, the beat frequency can be digitized by an analog-to-digital converter (ADC) 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 if the target has a velocity relative to LiDAR system 100, the target echo signal 202 will typically also include a frequency shift (Doppler shift). The Doppler shift can be determined separately and can be used to correct the frequency of the echo signal; therefore, for simplicity and ease of interpretation, in Figure 2 The Doppler shift is not shown. It should also be noted that the ADC's sampling frequency will determine the highest beat frequency that the system can process without aliasing. Generally, the highest frequency that can be processed is half the sampling frequency (i.e., the "Nyquist limit"). In one example, and not limited to, if the ADC's sampling frequency is 1 GHz, the highest beat frequency (Δf) that can be processed without aliasing is... Rmax The maximum frequency is 500MHz. This limitation further determines the maximum range of the system to be R. max =(c / 2)(Δf Rmax / k), which can be adjusted by changing the chirp slope k. In one example, although the data samples from the ADC can be continuous, the subsequent digital processing described below can be divided into “time periods” that can be associated with some periodicity in the LiDAR system 100. In one example, but not limited to, the time period can correspond to a predetermined number of chirp periods T, or the number of full rotations of the optical scanner in azimuth.

[0038] Figure 3A Figure 300a illustrates an example of the received signal power spectral density (PSD) 301a in a LiDAR system when the scanning mirror has a low rate. Figure 3BThis is a diagram illustrating an example of the received signal power spectral density (PSD) in a LiDAR system when the scanning mirror has a high angular rate. Scanning LiDAR systems (e.g., FMCW LiDAR) can use a moving scanning mirror to guide the beam and scan a target or target environment. To achieve a wide field of view and high frame rate, the scanning mirror can have a high angular rate. In some scenarios, a high mirror angular rate can lead to several impairments. For example, the Doppler shift caused by the mirror can widen the received signal bandwidth. Consequently, in these scenarios, the received signal strength can be reduced, and therefore the detection probability can be reduced, leading to an increase in errors related to range, velocity, and reflectivity measurements.

[0039] refer to Figure 3A and Figure 3B The moving scanning mirror (e.g., as) Figure 1A A scanning mirror included as part of system 100 may cause a Doppler shift in the outgoing and incoming beams, which can be a target echo signal. Figure 3A As depicted, when the scanning mirror is moving at a low mirror rate (e.g., <5 kdeg / s), the Doppler effect caused by the mirror has almost no impact on signal quality. A peak 302a can be detected in the PSD 301a of the received signal. The received signal may have a random implementation 305a, which can be small. The received signal has a reasonable range of frequency measurement error 303a and a reasonable range of power measurement error 304a.

[0040] like Figure 3B As described, when the scanning mirror is moving at a high mirror speed (>5 kdeg / s), there may be a significant broadening of the signal power spectral density (PSD) 301b. As a result, the measured signal energy may be lower on average. Consequently, the probability of detection may be reduced. Due to the randomness of the signal (e.g., random implementation 305b), the measurement error with respect to frequency 303b and / or with respect to energy 304b may be higher.

[0041] Figure 4 This is a diagram illustrating an example of a matched filter in a LiDAR system according to embodiments of the present disclosure. The embodiments described herein provide various approaches to counter-mirror Doppler spread. For example, embodiments may employ frequency domain techniques and time domain techniques. One approach in the frequency domain is matched filtering in the frequency domain. In this approach, the received signal is filtered in the frequency domain by a matched filter, wherein the matched filter includes the desired shape or waveform of the received signal in the frequency domain. The desired received signal frequency waveform can be determined based on theoretical models or simulations or measurements from predetermined conditions (e.g., conditions determined in a laboratory setting or test environment, artificial intelligence, etc.).

[0042] refer to Figure 4The received signal 401 in the frequency domain (e.g., the input spectrum) can be input to the matched filter 402. The received signal 401 may include a first frequency waveform, which may be an unknown waveform, for example, at the start of the matched filtering process. The matched filter 40 may include a second frequency waveform, which may be a waveform at the expected frequency of the received signal. 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 expected, estimated, or approximate first frequency waveform determined based on a theoretical model or experimental measurement. In some embodiments, the second frequency waveform may be determined based on a model, simulation, or measurement of the LiDAR system (e.g., the optical subsystem of the LiDAR system).

[0043] In one embodiment, the second frequency waveform can be an estimate based on the power spectral density (PSD) function of the received signal. For example, the matched filter 402 can include the expected received signal PSD. The matched filter 402 can be configured to compare the expected received signal PSD with the first frequency waveform and determine whether a match exists.

[0044] In one embodiment, the filter coefficients 403 of the matched filter 402 can be constant. For example, the filter coefficients 403 can be derived from theoretical simulation or modeling.

[0045] In one embodiment, the filter coefficients 403 can be updated based on a set of metrics. For example, the filter coefficients 403 can be updated depending on key factors such as the angular rate of the scanning mirror, the position of the scanning mirror, the optical scanner geometry, the scanning mirror size, the beam diameter, or the target. The set of metrics may include the angular rate of the scanning mirror, the position of the scanning mirror, the optical scanner geometry, the scanning mirror size, the beam diameter, or the target. For example, the filter coefficients 403 can be adapted or adjusted to better match the received signal. For example, the filter coefficients 403 can be initially determined from theoretical simulations or modeling and then dynamically updated or adjusted based on the angular rate of the scanning mirror, the position of the scanning mirror, the optical scanner geometry, or the target. For example, when the angular rate of the scanning mirror is fast, the filter coefficients 403 can be updated to widen the bandwidth of the matched filter.

[0046] In one embodiment, the matched filter coefficients 403 can be updated such that the matched filter bandwidth is proportional to the angular velocity of the scanning mirror, the size of the scanning mirror, and / or the beam diameter.

[0047] In one embodiment, the coefficient set can be updated based on changes in hardware configuration or system operation. For example, the coefficient set can be updated based on an increase in the angular rate of the mirror or a change in the scanning mode.

[0048] In one embodiment, the filter coefficients 403 can be updated continuously, for example, every 1 millisecond, 1 second, 15 seconds, 30 seconds, or any value in between. Alternatively, the filter coefficients 403 can be updated when changes are detected in the angular velocity of the scanning mirror, the position of the scanning mirror, the geometry of the optical scanner, or the target.

[0049] According to some embodiments, the matched filter 402 can be configured based on the convolutional waveform. For example, in one scenario, the matched filter 402 can be configured to compare the received signal (e.g., a first frequency waveform) with an expected received signal (e.g., a second frequency waveform) to determine the similarity between them. As an example, the matched filter 402 can be configured to calculate the cross-correlation between the received signal PSD and the expected received signal PSD. For example, the maximum correlation value can represent the peak value of the received signal.

[0050] If the second frequency waveform, which is the known waveform being filtered, is the complex conjugate of the received signal waveform, which is the unknown waveform, then the signal-to-noise ratio (SNR) and detection probability will be maximized by the matched filter 402. In one embodiment, the filtered received signal is input to a peak selection process to extract range and velocity information. A peak search 404 can be performed to detect peaks in the received signal. Then, the target's range and velocity information can be extracted based on the peaks in the received signal. Since the detection 405 occurs at the point where the SNR is maximized, this method can obtain more accurate frequency and energy measurements, thereby increasing the accuracy of target range and velocity measurements.

[0051] Figure 5 This is a diagram illustrating an example of a matched filter waveform according to an embodiment of the present disclosure. Different matched filter waveforms (e.g., a second frequency waveform) can be selected based on theoretical simulation or modeling, or different matched filter waveforms can be selected empirically. As an example, the matched filter may include a Gaussian waveform 501, where M(f) = exp(-0.5(f / B)). 2 M(f) = sinc(f / B) if |f| ≤ B; otherwise, M(f) = 0, where B determines the filter bandwidth. As another example, a matched filter may include a sinc squared waveform 502, where M(f) = sinc(f / B) if |f| ≤ B; otherwise, M(f) = 0, where B determines the filter bandwidth. As yet another example, a matched filter may include a sinc squared waveform 503, where M(f) = sinc(f / B) if |f| ≤ B. 2(f / B); otherwise, M(f) = 0, where B determines the filter bandwidth. As another example, a matched filter may include a rectangular waveform 504, where M(f) = 1 if |f| ≤ B; otherwise, M(f) = 0, where B determines the filter bandwidth. The above examples of matched filters can be defined by the parameter B that determines the filter bandwidth. In one embodiment, the filter bandwidth may be proportional to the angular rate of the scanning mirror. The above examples of matched filters are for illustrative purposes only. Many other matched filter waveforms may exist.

[0052] In digital signal processing, discrete frequency filter coefficients can be obtained by sampling continuous frequency waveforms (e.g., 501 to 504).

[0053] Figure 6 This is a flowchart illustrating an example of a process 600 for processing received signals in a LiDAR system according to an embodiment of the present disclosure. Process 600 may be performed by processing logic, which may include software, hardware, or a combination thereof. Software may be stored on a non-transitory machine-readable storage medium (e.g., a memory device). For example, it can be performed via... Figures 1A to 1B The matched filter module 130 in the signal processing unit 112 of the LiDAR system 100 shown performs processing 600. Through this processing, more accurate frequency and energy measurements can be achieved, thereby increasing the accuracy of target range and velocity measurements.

[0054] At box 601, the received signal is sampled at the LiDAR system and converted to the frequency domain, wherein the received signal includes a first frequency waveform.

[0055] At box 602, a matched filter is selected. The matched filter includes a second frequency waveform having a set of coefficients used to match the first frequency waveform. In one embodiment, the second frequency waveform is determined based on a model, simulation, or measurement of the optical subsystem of the LiDAR system. In another embodiment, the second frequency waveform is determined based on an estimate of the PSD of the received signal.

[0056] In one embodiment, selecting a matched filter includes selecting a rectangular waveform, a sinc waveform, a sinc squared waveform, or a Gaussian waveform as the second frequency waveform. In another embodiment, the matched filter includes at least one of a sinc waveform, a sinc squared waveform, a Gaussian waveform, and a rectangular waveform.

[0057] At block 603, the coefficient set is updated based on the metric set. In one embodiment, the coefficient set of the matched filter is updated based on at least one of the following: the angular rate of the scanning mirror, the position of the scanning mirror, the geometry of the optical scanner, and the target.

[0058] In one embodiment, the coefficient set is updated such that the filter bandwidth is proportional to the angular rate of the scanning mirror, the size of the scanning mirror, or the beam diameter. In another embodiment, the coefficient set is updated based on changes in hardware configuration or system operation, including changes in the angular rate of the mirror or changes in the scanning mode.

[0059] At box 604, the received signal can be filtered by a matched filter to generate a filtered received signal.

[0060] At block 605, range and velocity information are extracted from the filtered received signal. In one embodiment, the filtered received signal is input to a peak selection process to extract range and velocity information. For example, the peak value of the filtered received signal is detected to extract the range and velocity information of the target.

[0061] The foregoing description sets forth numerous specific details, such as examples of specific systems, components, methods, etc., to provide a thorough understanding of several examples in this disclosure. However, it will be apparent to those skilled in the art that at least some examples of this disclosure can be implemented without these specific details. In other instances, well-known components or methods have not been described in detail or presented in the form of simple block diagrams to avoid unnecessarily obscuring this disclosure. Therefore, the specific details set forth are merely exemplary. Specific examples may differ from these exemplary details and are still contemplated within the scope of this disclosure.

[0062] Any reference to "an embodiment" or "example" throughout this specification means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example. Therefore, the phrases "in an example" or "in the example" appearing in various places throughout this specification do not necessarily refer to the same example.

[0063] Although this document shows and describes the operations of the methods in a specific order, the order of the operations of each method can be changed so that some operations can be performed in reverse order, or so that some operations can be performed at least partially concurrently with other operations. Instructions or sub-operations of different operations can be performed intermittently or alternately.

[0064] The above description of the illustrated implementations of the invention (including those described in the abstract) is not intended to exhaustively or limit the invention to the precise forms disclosed. While specific implementations and examples of the invention have been described herein for illustrative purposes, various equivalent modifications can be made within the scope of the invention, as will be appreciated by those skilled in the art. The terms “example” or “exemplary” are used herein to mean used as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as being more preferred or advantageous than other aspects or designs. Rather, the use of the terms “example” or “exemplary” is intended to present concepts in a concrete manner. As used herein, the term “or” means inclusive “or” rather than exclusive “or.” That is, unless otherwise specified or the context clearly indicates, “X includes A or B” means any natural inclusion arrangement. 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 above instances. Furthermore, the terms “a” and “an” as used in this application and the appended claims should generally be interpreted as meaning “one or more” unless otherwise specified or clearly indicated from the context as singular. Additionally, the terms “first,” “second,” “third,” “fourth,” etc., as used herein refer to labels used to distinguish between different elements and do not necessarily have a meaning according to the order of their numerical names.

Claims

1. A method in a LiDAR system, wherein LiDAR is light detection and ranging, the method comprising: Generate a light beam from a light source and emit the light beam toward a target; Based on the beam, an echo signal is received from the target; The echo signal is sampled at the LiDAR system and converted to the frequency domain, wherein the echo signal includes a first frequency waveform; Select a matched filter, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform; The matched filter is updated by updating the set of coefficients of the second frequency waveform according to a set of metrics, wherein the set of metrics includes the angular rate of the scanning mirror; The echo signal is filtered using the matched filter to generate a filtered incoming echo signal; and Extract range and velocity information from the filtered echo signal.

2. The method according to claim 1, wherein, Selecting a matched filter includes choosing a rectangular waveform, a sinc waveform, a sinc squared waveform, or a Gaussian waveform as the second frequency waveform.

3. The method according to claim 1, wherein, The set of coefficients is updated based on at least one of the following: the position of the scanning mirror, the geometry of the optical scanner, and the target.

4. The method according to claim 1, wherein, The set of coefficients is updated such that the filter bandwidth of the matched filter is proportional to at least one of the scanning mirror angular velocity, scanning mirror size, and beam diameter.

5. The method according to claim 1, wherein, The second frequency waveform is determined based on the estimation of the power spectral density function of the echo signal.

6. The method according to claim 1, wherein, The set of coefficients is updated based on changes in system operation or hardware configuration, including changes in the angular rate of the mirror or changes in the scanning mode.

7. The method according to claim 1 further includes inputting the filtered echo signal into peak selection processing to extract the range and velocity information.

8. The method according to claim 1, wherein, The second frequency waveform is determined based on a model, simulation, or measurement of the optical subsystem of the LiDAR system.

9. A LiDAR system, namely a light detection and ranging system, comprising: A light source, used to generate a beam of light to be emitted toward a target; A scanning mirror is used to scan the target; A photodetector for receiving an echo signal from the target based on the light beam; Memory; as well as A processor, which is operatively coupled to the memory to: The echo signal is sampled at the LiDAR system and converted to the frequency domain, wherein the echo signal includes a first frequency waveform. Select a matched filter, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform. The matched filter is updated by updating the coefficient set of the second frequency waveform according to a set of metrics, wherein the set of metrics includes the angular rate of the scanning mirror. The echo signal is filtered using the matched filter to generate a filtered echo signal. Extract range and velocity information from the filtered echo signal.

10. The LiDAR system according to claim 9, wherein, The second frequency waveform includes a rectangular waveform, a sinc waveform, a sinc squared waveform, or a Gaussian waveform as the second frequency waveform.

11. The LiDAR system according to claim 9, wherein, The set of coefficients is also updated based on at least one of the following: the position of the scanning mirror, the geometry of the optical scanner, and the target.

12. The LiDAR system according to claim 9, wherein, The set of coefficients is updated such that the filter bandwidth of the matched filter is proportional to at least one of the scanning mirror angular velocity, scanning mirror size, and beam diameter.

13. The LiDAR system according to claim 9, wherein, The second frequency waveform is determined based on the estimation of the power spectral density function of the echo signal.

14. The LiDAR system according to claim 9, wherein, The set of coefficients is updated based on changes in system operation or hardware configuration, including changes in the angular rate of the mirror or changes in the scanning mode.

15. The LiDAR system according to claim 9, wherein, The processor, which is operatively coupled to the memory, is also configured to input the filtered echo signal into a peak selection process to extract the range and velocity information.

16. The LiDAR system according to claim 9, wherein, The second frequency waveform is determined based on a model, simulation, or measurement of the optical subsystem of the LiDAR system.

17. A non-transitory machine-readable medium storing instructions that, when executed by a processor of a LiDAR system, i.e., a light detection and ranging system, cause the processor to: Generate a light beam from a light source and emit the light beam toward a target; Based on the beam, an echo signal is received from the target; The echo signal is sampled at the LiDAR system and converted to the frequency domain, wherein the echo signal includes a first frequency waveform; Select a matched filter, the matched filter including a second frequency waveform having a set of coefficients for matching the first frequency waveform; The matched filter is updated by updating the set of coefficients of the second frequency waveform according to a set of metrics, wherein the set of metrics includes the angular rate of the scanning mirror; The echo signal is filtered using the matched filter to generate a filtered echo signal; and Extract range and velocity information from the filtered echo signal.

18. The non-transitory machine-readable medium according to claim 17, wherein, The second frequency waveform includes a rectangular waveform, a sinc waveform, a sinc squared waveform, or a Gaussian waveform as the second frequency waveform.

19. The non-transitory machine-readable medium according to claim 17, wherein, The set of coefficients is also updated based on at least one of the following: the position of the scanning mirror, the geometry of the optical scanner, and the target.

20. The non-transitory machine-readable medium according to claim 17, wherein, The set of coefficients is updated such that the filter bandwidth of the matched filter is proportional to at least one of the scanning mirror angular velocity, scanning mirror size, and beam diameter.

21. The non-transitory machine-readable medium according to claim 17, wherein, The second frequency waveform is determined based on the estimation of the power spectral density function of the echo signal.

22. The non-transitory machine-readable medium according to claim 17, wherein, The set of coefficients is updated based on changes in system operation or hardware configuration, including changes in the angular rate of the mirror or changes in the scanning mode.

23. The non-transitory machine-readable medium according to claim 17, wherein, The processor is also used to input the filtered echo signal into peak selection processing to extract the range and velocity information.

24. The non-transitory machine-readable medium according to claim 17, wherein, The second frequency waveform is determined based on a model, simulation, or measurement of the optical subsystem of the LiDAR system.

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