Selective Sub-Band Processing for Angular Resolution and Detection Sensitivity in LIDAR Systems

Abstract

A signal processing system includes a time domain processing component for: receiving range-related time domain baseband signal samples in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, separating the baseband signal samples into frequency subbands in the time domain based on a subband classification criterion, and selecting subband processing parameters for the frequency subbands in the time domain and the frequency domain based on the subband classification criterion. The subband processor selectively processes the frequency subbands in the time domain and the frequency domain based on the processing parameters.

Description

[0001] Related Applications

[0002] Pursuant to 35 U.S.C. §119(e), this application claims the benefit of U.S. Patent Application No. 16 / 999,954, filed on August 21, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to light detection and ranging (LIDAR) systems, and more particularly to improving angular resolution and detection sensitivity in LIDAR systems by selectively processing low-frequency and high-frequency sub-bands in the time and frequency domains. Background Art

[0004] Frequency modulated continuous wave (FMCW) LIDAR systems use a tunable laser to frequency-chirped illuminate the target and a coherent receiver to detect the backscattered or reflected light from the target, which is combined with a local replica of the transmitted signal. The local replica is mixed with the return signal delayed by the round-trip time to the target and back, generating a beat frequency for each target at the receiver.

[0005] There is a linear relationship between the beat frequency and the distance of a given target, so the target distance can be determined by measuring the beat frequency. Typically, close targets generate a strong, low beat frequency signal at the receiver, while far targets generate a weaker, higher beat frequency signal at the receiver.

[0006] Conventional signal processing methods that increase the probability of detecting weak return signals from distant targets can degrade measurements of other metrics such as angular resolution and range resolution, which can reduce reliable detection of close-range targets. Summary of the invention

[0007] The present disclosure describes various examples of LIDAR systems and methods for selectively processing sub-bands in a LIDAR system to achieve a high probability of detection of distant targets while increasing angular resolution and range resolution of close targets.

[0008] In one example, a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes a processor and a memory for storing instructions, which, when executed by the processor, cause the system to: receive range-related time-domain baseband signal samples, separate the baseband signal samples into a plurality of subbands in the time domain using a subband generator, classify the subbands into a plurality of subband types based on a subband classification criterion using a subband discriminator, and select subband processing parameters for the subbands in the time domain and the frequency domain based on the plurality of subband types. In one example, a plurality of subband processors are coupled to the subband generator and the subband discriminator to process the subbands in the time domain and the frequency domain based on the subband processing parameters.

[0009] In one example, the bandwidth and center frequency combination of each subband includes a first subband processing parameter, and the processor causes each of the plurality of subband processors to: generate a time domain sample block in a block sampler, wherein an integration time of the block sampler includes a second subband processing parameter based on a target detection probability associated with each subband type; and perform a discrete Fourier transform (DFT) on the time domain sample block using a DFT processor to generate a subband in the frequency domain, wherein a length of the DFT includes a third subband processing parameter based on a range resolution associated with each subband type, and a periodicity (frequency) of the DFT includes a fourth subband processing parameter based on an angular resolution associated with each subband type.

[0010] In one example, the processor causes each of the plurality of sub-band processors to: use a peak search processor to search for a signal peak, a signal-to-noise ratio (SNR) peak, and / or an average SNR above a threshold SNR in each frequency domain sub-band. The sub-band processor also filters each frequency domain sub-band in the frequency domain processor using a digital filter, wherein the length of the digital filter includes a fifth sub-band processing parameter based on the sub-band type. In one example, the sub-band classification criteria are based on one or more of a peak signal energy in the sub-band, an average signal-to-noise ratio in the sub-band, and a peak signal-to-noise ratio in the sub-band. In one example, the sub-band classification criteria are also based on one or more of scene features, relative target speed (EGO speed), azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

[0011] In one example, a first type of sub-band processed by a first type of sub-band processor includes a sub-band containing frequencies below a first frequency threshold corresponding to a predetermined short-range target, a second type of sub-band processed by a second type of sub-band processor includes a sub-band containing frequencies above a second frequency threshold corresponding to a predetermined long-range target, a third type of sub-band processed by a third type of sub-band processor includes a sub-band containing frequencies between the first frequency threshold and the second frequency threshold corresponding to a predetermined medium-range target, and the sub-band classification criteria include one or more of a center frequency of the sub-band and a bandwidth of the sub-band.

[0012] In one example, the fourth type of sub-band processed by the fourth type of sub-band processor includes a sub-band containing a peak energy higher than a first energy threshold corresponding to a predetermined short-range target, the fifth type of sub-band processed by the fifth type of sub-band processor includes a sub-band containing a peak energy lower than a second energy threshold corresponding to a predetermined long-range target, and the sixth type of sub-band processed by the sixth type of sub-band processor includes a sub-band containing a peak energy between the first energy threshold and the second energy threshold corresponding to a predetermined medium-range target.

[0013] In one example, the seventh type of sub-band processed by the seventh type of sub-band processor includes a sub-band including an average SNR higher than a first SNR threshold corresponding to a predetermined short-range target, the eighth type of sub-band processed by the eighth type of sub-band processor includes a sub-band including an average SNR lower than a second SNR threshold corresponding to a predetermined long-range target, and the ninth type of sub-band processed by the ninth type of sub-band processor includes a sub-band including an average SNR threshold between the first SNR threshold and the second SNR threshold corresponding to a predetermined medium-range target.

[0014] In one example, a method in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes: receiving range-related time-domain baseband signal samples; separating the baseband signal samples into subbands in the time domain; classifying the subbands into a plurality of subband types based on a subband classification criterion; selecting subband processing parameters for the subbands in the time domain and the frequency domain based on the plurality of subband types; and processing the subbands in the time domain and the frequency domain using the selected subband processing parameters.

[0015] In one example, the bandwidth and center frequency combination of each subband includes a first subband processing parameter, and processing the subbands in the time domain includes: generating a block of time domain samples in a block sampler, wherein an integration time of the block sampler includes a second subband processing parameter based on a target detection probability associated with each subband type; and performing a discrete Fourier transform (DFT) on the block of time domain samples using a DFT processor to generate subbands in the frequency domain, wherein a length of the DFT includes a third subband processing parameter based on a range resolution associated with each subband type, and a periodicity (frequency) of the DFT includes a fourth subband processing parameter based on an angular resolution associated with each subband type.

[0016] In one example, processing the subbands in the frequency domain includes: filtering each frequency domain subband in the frequency domain processor using a digital filter, wherein the length of the digital filter includes a fifth subband processing parameter based on the subband type; and searching for a signal peak, a signal-to-noise ratio (SNR) peak, or an average SNR above a threshold SNR in each frequency domain subband using a peak search processor.

[0017] In one example, the sub-band classification criteria are based on the peak signal energy in the sub-band, the average signal-to-noise ratio in the sub-band, and / or the peak signal-to-noise ratio in the sub-band. In one example, the sub-band classification criteria are also based on scene features, relative target speed (EGO speed), azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

[0018] In one example, an FMCW LIDAR system includes a time domain processing subsystem for receiving range-related time domain baseband signal samples in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, preconditioning the samples, and combining the preconditioned samples into a block of time domain samples. The example system also includes a discrete Fourier transform (DFT) processor coupled to the time domain processing subsystem for performing a DFT to convert the block of time domain samples into frequency domain subbands of a baseband signal. The example system also includes a frequency domain processing subsystem coupled to the DFT processor and the time domain processing subsystem for generating subbands in the frequency domain, classifying the subbands into a plurality of subband types based on a subband classification criterion, and processing each subband using a subband processing parameter based on the subband type.

[0019] In one example, the time domain processing subsystem includes: a time domain preprocessor for preconditioning the range-related baseband signal samples; and a sample block generator coupled to the preprocessor and the frequency domain processing subsystem to combine the preconditioned samples into the time domain sample blocks, wherein an integration time of the block sampler includes a first subband processing parameter based on a detection probability associated with each subband type.

[0020] In one example, the length of the DFT includes a second subband processing parameter based on a range resolution associated with each subband type, and the periodicity (frequency) of the DFT includes a third subband processing parameter based on an angular resolution associated with each subband type.

[0021] In one example, the frequency domain processing subsystem includes a subband generator to generate subbands in the frequency domain, wherein the bandwidth and center frequency combination of each subband includes a fourth subband processing parameter based on the target distance. In one example, the system also includes a subband discriminator coupled to the subband generator and the sample block generator to classify the subbands according to type and select the subband processing parameter for each subband in the frequency domain based on the subband type. In one example, the system also includes a plurality of frequency domain processing chains coupled to the subband generator and the subband discriminator to adjust each subband, detect a signal peak and a signal-to-noise ratio (SNR) peak and an average signal-to-noise ratio in each subband, and perform post-processing on each subband.

[0022] In one example, each frequency domain processing chain includes: a frequency domain processor to filter each subband based on the subband type, wherein the length of the digital filter in the frequency domain processor includes a fifth subband processing parameter based on the subband type; a peak search processor coupled to the frequency domain processor to selectively search for energy peaks based on the subband type; and a frequency domain post-processor for selectively filtering points in the LIDAR point cloud to remove noise.

[0023] In one example, the sub-band classification criteria are based on the peak signal energy in the sub-band, the average signal-to-noise ratio in the sub-band, and / or the peak signal-to-noise ratio in the sub-band. In one example, the sub-band classification criteria are also based on scene features, relative target speed (EGO speed), azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

[0024] In one example, a method in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes: receiving range-related time-domain baseband signal samples in a time-domain processing subsystem; combining the samples into sample blocks in the time domain using a sample block generator; transforming the sample blocks from the time domain to the frequency domain using a discrete Fourier transform (DFT) in a DFT processor; generating subbands in the frequency domain using a subband generator; classifying the subbands into a plurality of subband types based on a subband classification criterion using a subband discriminator; selecting subband processing parameters based on the subband type; and processing each frequency subband using the selected subband processing parameters on the corresponding subband.

[0025] In one example, the integration time of the block sampler includes a first subband processing parameter based on the detection probability associated with each subband type, the length of the DFT includes a second subband processing parameter based on the range resolution associated with each subband type, the periodicity (frequency) of the DFT includes a third subband processing parameter based on the angular resolution associated with each subband type, and the bandwidth and center frequency combination of each subband includes a fourth subband processing parameter.

[0026] In one example, processing each frequency subband includes: selectively filtering each subband according to a subband type, wherein the length of a digital filter in a frequency domain processor includes a fifth subband processing parameter based on a bandwidth associated with each subband type; selectively searching for energy peaks, signal-to-noise ratio (SNR) peaks, and averages in the frequency subband according to the subband type; and selectively filtering points in the LIDAR point cloud to remove noise. In one example, the subband classification criteria are based on peak signal energy in the subband, average signal-to-noise ratio in the subband, and peak signal-to-noise ratio in the subband. In one example, the subband classification criteria are also based on scene features, relative target speed (EGO speed), azimuth scan angle, altitude scan angle, and predicted targets based on previous frames. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] For a more complete understanding of various examples, reference is now made to the following detailed description taken in conjunction with the accompanying drawings, wherein like reference numerals correspond to like elements:

[0028] Figure 1 is a block diagram illustrating an example LIDAR system according to the present disclosure;

[0029] Figure 2 is a time-frequency diagram showing one example of a LIDAR waveform according to the present disclosure;

[0030] Figure 3 is a block diagram illustrating an example LIDAR system using selective sub-band processing in the time and frequency domains according to the present disclosure;

[0031] Figure 4 is a block diagram illustrating an example of a subband generator in the time domain according to the present disclosure;

[0032] Figure 5 is a block diagram illustrating another example of a subband generator in the time domain according to the present disclosure;

[0033] Fig. 6A -6C is an amplitude frequency diagram showing an example of subband selection according to the present disclosure;

[0034] Fig. 7A is a time-amplitude diagram illustrating one type of selective sub-band signal processing in the time domain for improving angular resolution of an object in accordance with the present disclosure;

[0035] Figure 7B is a time amplitude diagram illustrating one type of selective sub-band signal processing in the time domain for improving target detection probability according to the present disclosure;

[0036] Figure 8 is a flow chart illustrating an example method for selective subband processing in the time and frequency domains according to the present disclosure;

[0037] Fig. 9 is a block diagram illustrating an example LIDAR system using selective sub-band processing in the frequency domain according to the present disclosure;

[0038] Fig.10 is a block diagram illustrating a sub-band discriminator according to the present disclosure;

[0039] Fig.11 is a flow chart illustrating an example method of selective subband processing in the frequency domain according to the present disclosure; and

[0040] Fig.12 is a block diagram illustrating an example processing system according to the present disclosure. DETAILED DESCRIPTION

[0041] The present disclosure describes examples of LIDAR systems and methods therein for selectively processing frequency sub-bands of target return signals generated in the time domain and / or frequency domain to increase the angular resolution and range resolution of close targets while increasing the probability of detecting distant targets. According to some embodiments, the described LIDAR system can be implemented in any sensing market, such as but not limited to transportation, manufacturing, metrology, medical, and security systems. According to some embodiments, the described LIDAR system is implemented as part of a frequency modulated continuous wave (FMCW) device front end, which facilitates spatial perception of an autonomous driving assistance system or an autonomous vehicle.

[0042] Figure 1 FIG. 1 shows a LIDAR system 100 according to an example embodiment of the present disclosure. The LIDAR system 100 includes one or more of a number of components, but may include more than Figure 1 As shown, LIDAR system 100 includes an optical circuit 101 implemented on a photonic chip. Optical circuit 101 may include a combination of active optical components 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.

[0043] The free space optical system 115 may include one or more optical waveguides to carry optical signals and route and manipulate the optical signals to appropriate input / output ports of active optical circuits. The free space optical system 115 may also include one or more optical components, such as taps, wavelength division multiplexers (WDMs), splitters / combiners, polarization beam splitters (PBSs), collimators, couplers, etc. In some examples, for example, the free space optical system 115 may include components for transforming polarization states and guiding received polarized light to an optical detector using a PBS. The free space optical system 115 may also include a diffractive element for deflecting light beams with different frequencies at different angles along an axis (e.g., a fast axis).

[0044] In some examples, the LIDAR system 100 includes an optical scanner 102, which includes one or more scanning mirrors rotatable along an axis (e.g., a slow axis) that is orthogonal or substantially orthogonal to the fast axis of the diffraction element, for manipulating the optical signal to scan the environment according to a scanning pattern. For example, the scanning mirror can be rotated by one or more galvanometers. The optical scanner 102 also collects light incident on any object in the environment into a return beam that is returned to the passive optical circuit components of the optical circuit 101. For example, the return beam can be directed to an optical detector by a polarization beam splitter. In addition to the scanning mirror and galvanometer, the optical scanner 102 may include components such as a quarter wave plate, a lens, and a window covered with an anti-reflection coating.

[0045] To control and support the optical circuit 101 and the optical scanner 102, the LIDAR system 100 includes a LIDAR control system 110. The LIDAR control system 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, a central processing unit, and the like. More specifically, the processing device 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 that executes other instruction sets, or a processor that executes 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), a network processor, and the like. In some examples, the LIDAR control system 110 may include a memory for storing data and instructions to be executed by the processing device described herein. The memory can be, for example, read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic disk storage such as a hard disk drive (HDD), optical disk storage such as compact disk read-only memory (CD-ROM) and compact disk read-write (CD-RW), or any other type of non-transitory memory.

[0046] In some examples, the LIDAR control system 110 may include a signal processing unit 112, such as a DSP. The LIDAR control system 110 is configured to output a digital control signal to control the optical driver 103. In some examples, the digital control signal may be converted into an analog signal by a signal conversion unit 106. For example, the signal conversion unit 106 may include a digital-to-analog converter. The optical driver 103 may then provide a drive signal to the active optical components of the optical circuit 101 to drive a light source (such as a laser and an amplifier). In some examples, several optical drivers 103 and signal conversion units 106 may be provided to drive multiple light sources.

[0047] 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 can convert coordinate routing information from the LIDAR control system 110 into a signal interpretable by the galvanometer in the optical scanner 102. In some examples, the motion control system 105 can also return information about the position or operation of components of the optical scanner 102 to the LIDAR control system 110. For example, the analog-to-digital converter can in turn convert information about the position of the galvanometer into a signal interpretable by the LIDAR control system 110.

[0048] The LIDAR control system 110 is also configured to analyze the input digital signal. In this regard, the LIDAR system 100 includes an optical receiver 104 to measure one or more beams received by the optical circuit 101. For example, a reference beam receiver can measure the amplitude of a reference beam from an active optical component, and an analog-to-digital converter converts the signal from the reference receiver into a signal interpretable by the LIDAR control system 110. The target receiver measures the optical signal, which carries information about the distance and speed of the target in the form of a beat frequency modulated optical signal. The reflected beam can be mixed with a second signal from a local oscillator. The optical receiver 104 can 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 can be subjected to signal conditioning by a signal conditioning unit 107 before being received by the LIDAR control system 110. For example, the signal from the optical receiver 104 can be provided to an operational amplifier for amplifying the received signal, and the amplified signal can be provided to the LIDAR control system 110.

[0049] 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 send the images and locations or information related thereto to the LIDAR control system 110 or other systems connected to the LIDAR system 100.

[0050] In operation according to some examples, LIDAR system 100 is configured to use a non-degenerate light source to simultaneously measure distance and velocity across two dimensions. This capability allows real-time remote measurement of distance, velocity, orientation, and altitude of the surrounding environment.

[0051] In some examples, the scanning process begins with the optical driver 103 and the LIDAR control system 110. The LIDAR control system 110 instructs the optical driver 103 to independently modulate one or more light beams, and these modulated signals are propagated to the collimator through the passive optical circuit. The collimator guides the light at the optical scanning system, which scans the environment through a pre-programmed pattern defined by the motion control system 105. The optical circuit 101 may also include a polarization wave plate (PWP) to convert the polarization of the light when it leaves the optical circuit 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 circuit 101. For example, a lens or collimation system used in the LIDAR system 100 may have a natural reflective property or a reflective coating to reflect a portion of the light back to the optical circuit 101.

[0052] The light signal reflected back from the environment passes through the optical circuit 101 to the receiver. Because the polarization of the light has been converted, it can be reflected by the polarization beam splitter together with the portion of the polarized light reflected back to the optical circuit 101. Accordingly, the reflected light is reflected to a separate optical receiver instead of returning to the same optical fiber or waveguide as the light source. These signals interfere with each other and generate a combined signal. Each beam signal returned from the target generates a time-shifted waveform. 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 to the optical receiver 104.

[0053] The analog signal from the optical receiver 104 is converted to 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 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 the galvanometer (not shown) and image data from the image processing system 114. As the optical scanner 102 scans additional points, the signal processing unit 112 can then generate a 3D point cloud with information about the distance and speed of the points in the environment. The signal processing unit 112 can also overlay the 3D point cloud data with the image data to determine the speed and distance of objects in the surrounding area. The system also processes satellite-based navigation position data to provide accurate global position.

[0054] Figure 2 FIG. 2 is a time-frequency diagram 200 of an FMCW scanning signal 201 that may 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 FM (t)) is a sawtooth waveform (sawtooth "chirp") with a chirp bandwidth Δf C and chirp period T CThe slope of the sawtooth is expressed as k = (Δf C / T C ).

[0055] Figure 2 Also depicted is a target return signal 202 according to some embodiments. The target return signal 202 (labeled as f FM (t-Δt)) is the time-delayed version of the scanning signal 201, where Δt is the round-trip time to and from the target illuminated by the scanning signal 201. The round-trip time is expressed as Δt=2R / v, where R is the target distance and v is the speed of the light beam, i.e., the speed of light c. The target distance R can therefore be calculated as R=c(Δt / 2). When the return signal 202 is optically mixed with the scanning signal, a distance-dependent difference frequency ("beat frequency") Δf is generated. R (t). Beat frequency Δf R (t) is linearly related to the delay Δt of the sawtooth slope k. In other words, Δf R (t) = kΔt. Since the target distance R is proportional to Δt, the target distance R can be calculated as R = (c / 2)(Δf R (t) / k). In other words, the distance R is related to the beat frequency Δf R (t) is linearly related. For example, the beat frequency Δf can be generated R (t) as an analog signal in the optical receiver 104 of the system 100. The beat frequency may then be digitized, for example, by an analog-to-digital converter (ADC) in a signal conditioning unit such as the signal conditioning unit 107 in the LIDAR system 100. The digitized beat frequency signal may then be digitally processed, for example, in a signal processing unit such as the signal processing unit 112 in the system 100. It should be noted that if the target has a velocity relative to the LIDAR system 100, the target return signal 202 will typically also include a frequency offset (Doppler shift). The Doppler shift may be determined separately and used to correct the frequency of the return signal, so for simplicity and ease of explanation, the Doppler shift is referred to in Figure 2 It should also be noted that the sampling frequency of the ADC will determine the highest beat frequency that can be handled by the system without aliasing. Generally speaking, the highest frequency that can be handled is one-half the sampling frequency (i.e., the "Nyquist limit"). In one example, and without limitation, if the sampling frequency of the ADC is 1 GHz, then the highest beat frequency (Δf Rmax ) is 500 MHz. This limit in turn determines the maximum distance 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, while the data samples from the ADC may be continuous, the subsequent digital processing described below may be divided into "time periods" that may be associated with some periodicity in the LIDAR system 100. In one example, and without limitation, a time period may correspond to a predetermined number of chirp periods T, or a number of complete rotations of the optical scanner in azimuth.

[0056] Figure 3 is a block diagram of an example signal processing system 300 that uses selective time-domain and frequency-domain processing to enhance detectability of distant targets and improve range and angular resolution of close targets. System 300 may be, for example, a subsystem or component of signal processing unit 112 of LIDAR system 100. System 300 may be implemented in hardware, firmware, software, or some combination of hardware, firmware, and software.

[0057] In one example, the signal processing system 300 includes a time domain processing subsystem 310, a subband processor 320 for processing one or more subbands of a first type, a subband processor 330 for processing one or more subbands of a second type, and a subband processor 340 for processing one or more subbands of a third type.

[0058] In one example, the time domain processing subsystem 310 is configured to receive a range-dependent time domain baseband signal Δf in a frequency modulated continuous wave (FMCW) LIDAR system (eg, system 100 ). R(t) to separate the baseband signal into frequency subbands in the time domain based on a subband classification criterion, and to select subband processing parameters for the frequency subbands in the time domain and the frequency domain based on the subband classification criterion.

[0059] In one example, the time domain processing subsystem 310 includes a time domain preprocessor 311 to condition the distance-related baseband signal samples. Examples of signal conditioning include, but are not limited to, band limiting and band forming, such as Hamming window or Heining window, to reduce noise and undesirable signal conversion artifacts in subsequent signal processing operations (such as time-to-frequency conversion). The preconditioned time domain samples are then provided to a subband generator 312, which generates time domain subbands with N limiting frequencies of up to N different center frequencies and N different bandwidths (where N is an integer higher than 1), where the subbands may or may not overlap. The subbands may cover from 0 to Δf Rmax The full spectrum of the baseband signal of FIG. 1 is shown in FIG. 1 , wherein the lower frequency sub-bands correspond to targets close to the LIDAR system 100 and the higher frequency sub-bands correspond to targets far away from the LIDAR system 100 .

[0060] Typically, low frequency sub-bands from close targets contain high energy signals that are easy to detect, while high frequency sub-bands from distant targets contain low energy signals (due to signal dispersion and path loss) and are more difficult to detect. Additionally, targets that are close to the LIDAR system 100 require higher range and angular resolution than distant targets because their closeness in range corresponds to closeness in time, and the LIDAR system 100 must be able to accurately track and avoid these targets.

[0061] The sub-bands can be classified according to type. In one example, the first type of sub-band (type 1) may include a sub-band containing a frequency lower than a predetermined low-frequency threshold corresponding to the maximum close-range target distance, the second type of sub-band (type 2) may include a sub-band containing a frequency higher than a predetermined high-frequency threshold corresponding to the minimum long-range target distance, and the third type of sub-band (type 3) may include a sub-band containing a frequency between the low-frequency threshold and the high-frequency threshold corresponding to the medium-range target distance. The sub-bands within each type of sub-band processor may have a fixed or variable bandwidth, and may be overlapping or non-overlapping, and have a fixed or variable center frequency. Indicators other than or in addition to frequency may be used to define sub-bands. For example, sub-bands may be defined by peak or average signal energy or peak or average signal-to-noise ratio. Indicators may also include information about the position of the target in azimuth and altitude and the angular velocity and radial velocity of the target based on the previous scan. It should be understood that the use of three sub-band processors in the example of system 300 is only for ease of illustration and example, and is not intended to be limiting.

[0062] Figure 4 4 is a block diagram of an example time domain subband generator 400 according to some embodiments, which can generate time domain subbands similar to those described above with respect to the time domain subband generator 312. The subband generator 400 includes a group 401 of N downconverters (0 to (N-1)) to convert an input signal Δf R(t) The downconverter signal is then band-limited and downsampled by a group 402 of N filters and downsamplers to generate N subbands 0 to N-1. Overlapping subbands can be created by adjusting the downconverter frequency and the filter bandwidth. In some examples, the center frequencies of the subbands can be evenly spaced, and the bandwidths of the N subbands can all be the same. In other examples, the center frequencies of the subbands can be non-uniformly spaced (e.g., increasing spacing as frequency increases), and the bandwidths of the subbands can be non-uniform (e.g., increasing as the center frequency of the subband increases).

[0063] Figure 5is a block diagram of an example time domain subband generator 500 according to some embodiments, which can generate uniform or non-uniform time domain subbands as described above with respect to the time domain subband generator 312. The subband generator 500 includes a 1:N downsampler 501 that receives a sampled input signal Δf R(t) And the input signal is distributed to the filter bank 502 so that filter 0 receives samples 0, N, 2N, ..., filter 1 receives samples 1, N+1, 2N+1, ..., etc. The center frequencies of the filters in the filter bank 502 can be uniformly or non-uniformly spaced and have uniform or non-uniform bandwidths as described with respect to the subband generator 400. The output of the filter bank 502 is fed to a discrete Fourier transform engine 503, which can perform a fast Fourier transform (FFT).

[0064] Although the frequency content of the baseband signal may include frequencies from zero to a maximum frequency Δf Rmax The energy at all frequencies (corresponding to the maximum distance of the LIDAR system 100 ), but not all frequencies may be present due to the absence of a target at the corresponding distance. Fig. 6A , 6B 6C show examples of different ways of generating subbands according to some embodiments, where the baseband signal energy is limited to three subbands. Fig. 6A The spectrum 601 of the simplest case is shown, where the subbands have uniform bandwidth and do not overlap, and the signal energy is confined to subbands n-1, n and n+1 (where 0 <n<N)。 Figure 6B Spectrum 602 is shown for a uniform bandwidth case with uniform overlap, which may be used to avoid performance loss at sub-band edges. Figure 6C A spectrum 603 is shown for a third case, where the subbands have non-uniform bandwidths and non-uniform overlaps, with Figure 6B This can reduce processing overhead by comparison.

[0065] Back to Figure 3, the subband generator 312 may be configured to provide a type 1 subband to a type 1 subband processor 320, a type 2 subband to a type 2 subband processor 330, and a type 3 subband to a type 3 subband processor 340. In addition, the time domain processing subsystem 310 may further include a subband discriminator 313 for detecting the subbands generated by the subband generator 312 and selecting processing parameters for the subbands in the subband processors 320 and 330 in the time domain and the frequency domain based on the subband type and the additional subband classification criteria. In addition to frequency, the subband classification criteria may include, but are not limited to, peak or average signal energy and / or peak or average signal-to-noise ratio. In other examples, the subband classification criteria may also include characteristics of the target environment (scene characteristics), such as the density of targets, the relative radial target velocity (EGO velocity) derived from the Doppler shift, the angular velocity, the position in the scanned field of view (FOV) based on the azimuth and elevation angles, and the predicted position of the target based on the previous scan.

[0066] As described above, the type 1 subband is coupled to the type 1 subband processor 320, the type 2 subband is coupled to the type 2 subband processor 330, and the type 3 subband is coupled to the type 3 subband processor 340. Figure 3 In the example of , the type 1 subband processor 320 includes k processing chains 1 to k, the type 2 subband processor 330 includes k processing chains k+1 to m, and the type 3 subband processor 340 includes n processing chains m+1 to n. Each processing chain in the subband processor includes a block sampler 321, a discrete Fourier transform (DFT) processor 322, a peak search processor 323, and a frequency domain (FD) processor 324. The subband processing parameters of each processing chain are controlled by the subband discriminator 313 based on the subband classification criteria, using data and control lines 340. As described below, the subband processing parameters may include the adaptation bandwidth of each subband, the integration time of each block sampler, the length and period of each DFT, and the length of the digital filter in the frequency domain processor. The following is a description of a typical processing chain in one of the subband processors 320, 330 or 340.

[0067] In one example, the subband processor receives and processes a subband sample stream from the subband generator 312 via a data line 350, wherein each subband sample in the sample stream has a duration corresponding to the sampling rate of the time domain baseband signal received by the time domain processing subsystem 310. The block sampler 321 integrates the subband samples into time domain sample blocks. The integration time of the block samples is one of the subband processing parameters controlled by the subband discriminator 313. For example, due to signal dispersion and path loss, the type 3 subband, i.e., the high frequency subband corresponding to the distant target, usually has low signal energy and low signal-to-noise ratio (SNR). Therefore, the integration time of the high frequency subband can be increased to extract the signal from the noise. In contrast, in the case of the lower frequency type 1 subband corresponding to the close target, the signal energy and SNR are usually high, so the integration time can be reduced. The integration time of the mid-range subband signal of type 2 will be between the integration time of the type 1 subband and the type 3 subband.

[0068] The integration time of the block sampler 321 is also related to the angular resolution. This is because the optical scanner in the LIDAR system, such as the optical scanner 102 in the example LIDAR system 100, has a high angular velocity (measured in degrees per second) in azimuth, so the angular resolution is directly proportional to time. Therefore, for close targets with high signal energy, reducing the integration time of the block sampler also improves the angular resolution.

[0069] The blocks of time domain samples generated by the block sampler 321 are received by a discrete Fourier transform (DFT) processor 322, which converts the blocks of time domain samples into sub-bands in the frequency domain with frequency resolution based on processing parameters provided by the sub-band discriminator 313. Two sub-band processing parameters may be associated with the DFT processor 322; the DFT length (i.e., the number of points in the DFT) and the DFT period (i.e., the duration of the DFT). For a block of time domain samples with a given integration time and a given bandwidth, a longer DFT generates a smaller frequency window and a higher frequency resolution, which translates into a higher range resolution in a FMCW LIDAR system. For a given DFT length, the period of the DFT will affect the angular resolution of the system, since time is converted to an angular rotation of a scanning mirror in an optical scanner.

[0070] Fig. 7A is to illustrate the use of a longer length DFT with a short duration (in Fig. 7A 16 points in the example above) to improve the range and angle resolution of the time-frequency diagram 701, which may be suitable for low-frequency signals associated with close-range targets. Figure 7B is to illustrate the use of a shorter DFT with a longer duration ( Figure 7B 4 points in the example, which may be applicable to higher frequency signals associated with distant targets.

[0071] Each processing chain in the sub-band processor includes a peak search processor 323 to search each frequency bin in the output of its corresponding DFT processor 322. Each peak search processor 323 performs a search for energy peaks, signal-to-noise ratio (SNR) peaks, and / or average SNRs in a frequency bin that may be associated with a target in the field of view (FOV) of the LIDAR system to establish a point in the 3D LIDAR cloud model of the target environment.

[0072] Each processing chain in the type 1 sub-band processor 320 may also include a frequency domain post-processor (FD processor) 324, which is configured to resolve the target distance and target angle in the 3D LIDAR point cloud. Examples of post-processing may include, but are not limited to, resampling, time averaging, and digital filtering of the frequency domain sub-band signals generated by the DFT processor 322. The length of the digital filter may be used by the sub-band discriminator as an additional sub-band processing parameter.

[0073] Figure 8 8 is a flow chart illustrating an example method 800 that may be implemented in an FMCW LIDAR system such as the system 300. The method 800 begins at operation 802 by receiving range-related time-domain baseband signal samples in a frequency modulated continuous wave (FMCW) LIDAR system in a time-domain processing component (e.g., the time-domain processing component 310). The method 800 continues at operation 804 by separating the baseband signal samples into subbands in the time domain (e.g., in the subband generator 312). The method 800 continues at operation 806 by classifying the subbands into a plurality of subband types based on a subband classification criterion (e.g., in the subband discriminator 313). The method 800 continues at operation 808 by selecting subband processing parameters for the subbands in the time domain and the frequency domain based on the plurality of subband types (e.g., in the subband discriminator 313). The method 800 ends at operation 810 by processing the subbands in the time and frequency domains with the selected subband processing parameters (eg, in the block sampler 321, the DFT processor 322, the peak search processor 323, and the frequency domain processor 324).

[0074] Fig. 9 is a block diagram illustrating an example signal processing system 900 that uses selective signal processing in the frequency domain to classify subbands according to a subband classification criterion and processes the subbands with selected subband processing parameters based on the subband classification criterion. System 900 may be, for example, a subsystem or component of signal processing unit 112 of LIDAR system 100. System 900 may be implemented in hardware, firmware, software, or some combination of hardware, firmware, and software.

[0075] In one example, the system 900 includes a time domain processing subsystem 910 to receive range-related time domain baseband signal samples in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, pre-condition the samples, and integrate the pre-conditioned samples into blocks of time domain samples. The example system 900 also includes a discrete Fourier transform (DFT) processor 920 coupled to the time domain processing component 910 to convert the blocks of time domain samples into frequency components (subbands) of the time domain baseband signal. The example system 900 also includes a frequency domain processing subsystem coupled to the DFT processor to generate subbands in the frequency domain, classify the subbands by type, and process each subband using a selected subband processing parameter based on the subband type.

[0076] In one example, the time domain processing subsystem 910 includes a time domain preprocessor 911 to adjust the range-dependent baseband signal samples. The time domain preprocessor 911 can be similar to the time domain preprocessor 311 in the system 300 described above.

[0077] The preconditioned time domain samples are then provided to a sample block generator 912 which integrates the samples into sample blocks in the time domain as described above with respect to block sampler 321 in system 300, where the integration time is a processing parameter selected based on the subband classification criteria described above with respect to system 300.

[0078] The sample blocks from the sample block generator 912 are provided to the DFT processor 920, which converts the time domain sample blocks into sub-bands in the frequency domain. The length and period of the DFT can also be sub-band processing parameters selected based on the sub-band classification criteria. In one example, the DFT processor 920 can be configured to convert the time domain block samples into the full spectrum of the baseband signal. For example, but not limited to, if the baseband spectrum covers 0 Hertz (Hz) to 500 Megahertz (MHz), the required Nyquist sampling rate in the time domain will be 1 Gigahertz (GHz) to avoid aliasing. If the DFT processor performs a 32-point DFT, the output of the DFT processor will be 16 frequency bins spanning 500MHz, where each frequency bin spans 31.25MHz individually.

[0079] The output of the DFT processor 920 is provided to a frequency domain processing subsystem 930. The frequency domain processing subsystem 930 includes a subband generator 931 for generating subbands in the frequency domain. The subband generator 931 can be configured to generate N subbands in the frequency domain, similar to the subband generator 312 in the above-mentioned system 300. The center frequency and bandwidth of the subband can also be subband processing parameters selected based on the subband classification criteria. The subband generator 931 can upsample or downsample the output of the DFT processor 920.

[0080] The frequency domain processing subsystem 930 also includes a subband discriminator 932 coupled to the subband generator 931, the sample block generator 912, and the DFT processor 920 to classify the subbands by type based on the subband classification criteria and select subband processing parameters for each subband in the frequency domain based on the subband type.

[0081] Fig.10 1 is a flow chart illustrating an example algorithm 1000 that may be used by the subband discriminator 932 to classify subbands for selective processing in accordance with some embodiments. For each subband, the subband discriminator 932 determines whether the peak energy in the subband is above a predetermined energy threshold in operation 1001, whether the average SNR in the subband is above a predetermined SNR threshold in operation 1002, or whether the peak SNR in the subband is above a predetermined peak SNR threshold in operation 1003. If any of these conditions are met, an OR operation 1004 selects the subband for further consideration and processing.

[0082] In parallel, the sub-band discriminator 932 processes the scene information to determine any key azimuth or elevation angles for which enhanced angular resolution is desired and any ranges for which enhanced range resolution is desired in operation 1005. The scene information may include, but is not limited to, scene characteristics (e.g., overall target density), target velocity, and detections or predictions of targets from previous LIDAR scan frames.

[0083] In operation 1006, any critical azimuth or elevation angles requiring high angular resolution and any critical ranges requiring high range resolution (sub-band frequencies) are compared to the real-time azimuth and elevation angles to classify the sub-bands based on the current scan area. The result is logically "AND" calculated with the result of OR operation 1004 in operation 1007 to classify the sub-bands. Next, in operation 1008, the number of critical (i.e., high-resolution) sub-bands is limited to a maximum number determined, for example, by system processing capabilities, and prioritized based on criticality (e.g., collision avoidance). The output of operation 1008 is the sub-band processing parameters used in the sub-band processing chain in the frequency domain processing component 930.

[0084] Back to Fig. 9 The frequency domain processing subsystem 930 also includes N frequency domain processing chains coupled to the subband generator 931 and the subband discriminator 932 to adjust each subband, detect an energy peak or a signal-to-noise ratio (SNR) peak in each subband, and resolve target distance and resolve target angle in each subband with a detectable energy peak or SNR peak.

[0085] Each frequency domain processing chain includes: a frequency domain (FD) pre-processor 933-n (where 1≤n≤N) for selectively filtering the sub-band based on the sub-band type; a peak search processor 934-n, which is coupled to the FD pre-processor 933-n, for selectively searching for energy peaks based on the sub-band type; and a frequency domain (FD) post-processor 935-n for selectively filtering points in a three-dimensional (3D) LIDAR point cloud to remove noise based on the sub-band type. Processing parameters for these operations, such as the length of digital filters for pre-processing and post-processing, can be selected by the sub-band discriminator 932 based on the sub-band classification criteria.

[0086] In one example, for a frequency subband containing frequencies below a specified upper frequency threshold, the FD pre-processor 933-n includes a filter having a bandwidth less than or equal to the bandwidth of the frequency subband, the peak search processor 934-n includes a peak search engine having a detection threshold above a predetermined lower threshold, and the frequency domain (FD) post-processor 935-n includes a point cloud filter having a noise suppression threshold above a predetermined upper threshold.

[0087] Fig.11 1 is a flow chart illustrating an example method 1100 for selectively processing subbands that may be implemented in an FMCW LIDAR system such as system 900. Method 1100 begins at operation 1102 by receiving range-dependent time-domain baseband signal samples in a time-domain processing subsystem (e.g., subsystem 910). Method 1100 continues at operation 1104 by combining the samples into sample blocks in the time domain (e.g., integrating the samples in sample block generator 912). Next, method 1100 continues at operation 1106 by transforming the sample blocks from the time domain to the frequency domain in a discrete Fourier transform (DFT) processor (e.g., DFT processor 920). At operation 1108, method 1100 continues by generating subbands in the frequency domain (e.g., in subband generator 931). At operation 1110, method 1100 continues by classifying the subbands into a plurality of subband types based on a subband classification criterion (e.g., in subband discriminator 932). The method 100 continues at operation 1112 by selecting subband processing parameters (e.g., subband bandwidth and frequency, integration time, DFT length and period, and digital filter length) based on the subband type (e.g., by the subband discriminator 932). The method 1100 ends with operation 1114 by processing each frequency subband (e.g., in processing chains 933-n, 934-n, and 935-n) using the selected subband processing parameters (e.g., from the subband discriminator 932) on the corresponding subband.

[0088] Fig.121 is a block diagram illustrating an example processing system 1200 for selectively processing frequency sub-bands in a LIDAR system according to an embodiment of the present disclosure. The processing system 1200 may include components that are the same or similar to the components of the signal conditioning unit 107 and the signal processing unit 112 in the LIDAR system 100. The processing system 1200 includes a processor 1201, which may be any type of general-purpose processing device or a dedicated processing device designed for use in a LIDAR system. The processing device 1201 is coupled to a memory 1202, which may be any type of non-transitory computer-readable medium (e.g., RAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic disk storage, or optical disk storage) containing instructions that, when executed by the processor 1201, cause the LIDAR system to perform the method 1100 described herein. Specifically, the memory 1202 includes instructions 1204 for receiving distance-related time domain baseband signal samples (e.g., in the time domain processing module 310); instructions 1206 for separating the baseband signal samples into frequency subbands in the time domain based on the subband classification criteria (e.g., in the time domain processing module 310); and instructions 1208 for selecting subband processing parameters for the frequency subbands in the time domain and the frequency domain based on the subband classification criteria (e.g., in the subband processors 320, 330 and 340).

[0089] The foregoing description sets forth many specific details, such as examples of specific systems, components, methods, etc., in order to provide a thorough understanding of several examples in the present disclosure. However, it will be apparent to those skilled in the art that at least some examples of the present disclosure may be practiced without these specific details. In other examples, well-known components or methods are not described in detail or presented in simple block diagram form to avoid unnecessarily obscuring the present disclosure. Therefore, the specific details set forth are merely exemplary. Specific examples may differ from these exemplary details and are still considered to be within the scope of the present disclosure.

[0090] Any reference throughout this specification to "one example" or "an example" means that a particular feature, structure, or characteristic described in conjunction with the example is included in at least one example. Therefore, the phrases "in one example" or "in an example" appearing in various places throughout this specification do not necessarily refer to the same example.

[0091] Although the operations of the methods herein are shown and described in a particular order, the order of the operations of each method can be changed so that some operations can be performed in a reverse order or so that some operations can be performed at least partially simultaneously with other operations. Instructions of different operations or sub-operations can be performed in an intermittent or alternating manner.

[0092] The above description of the illustrated embodiments of the present invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the present invention to the precise form disclosed. Although specific embodiments and examples of the present invention are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the present invention as will be appreciated by those skilled in the relevant art. The words "example" or "exemplary" used herein refer to examples, examples or illustrations. Any aspect or design described herein as "example" or "exemplary" is not necessarily to be interpreted as superior or advantageous to other aspects or designs. On the contrary, the use of the words "example" or "exemplary" is intended to present the concept in a specific way. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise stated or clear from the context, "X includes A or B" is intended to mean any natural inclusive arrangement. That is, if X includes A; X includes B; or X includes A and B, then "X includes A or B" is satisfied in any of the foregoing cases. In addition, the articles "one" and "an" used in this application and the appended claims should generally be interpreted as meaning "one or more", unless otherwise stated or clear from the context to point to a singular form. Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. used herein are intended as labels for distinguishing different elements, and do not necessarily have an ordinal meaning according to their numerical names.

Claims

1. A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system comprising: processor; a memory for storing instructions that, when executed by the processor, cause the system to: Receive distance-dependent time-domain baseband signal samples, Separating the baseband signal samples into subbands in the time domain using a subband generator, classifying the subband into a plurality of subband types based on a subband classification criterion using a subband discriminator, and selecting subband processing parameters for the subbands in the time domain and the frequency domain based on the plurality of subband types; as well as a plurality of subband processors coupled to the subband generator and the subband discriminator for processing the subbands in the time domain and the frequency domain based on subband processing parameters, wherein: The bandwidth and center frequency combination of each sub-band comprises a first sub-band processing parameter, and the processor causes each sub-band processor of the plurality of sub-band processors to: generating a block of time domain samples in a block sampler, wherein an integration time of the block sampler includes a second subband processing parameter based on a target detection probability associated with each subband type; as well as A discrete Fourier transform (DFT) is performed on the time domain sample block using a DFT processor to generate subbands in the frequency domain, wherein a length of the DFT includes a third subband processing parameter based on a range resolution associated with each subband type, and a frequency of the DFT includes a fourth subband processing parameter based on an angular resolution associated with each subband type.

2. The system according to claim 1, characterized in that The processor causes each sub-band processor of the plurality of sub-band processors to: searching, using a peak search processor, in each frequency domain subband for a signal peak, a signal-to-noise ratio (SNR) peak, and / or an average SNR above a threshold SNR; and Each frequency domain subband in the frequency domain processor is filtered using a digital filter, wherein a length of the digital filter includes a fifth subband processing parameter based on the subband type.

3. The system according to claim 1, characterized in that The sub-band classification criteria is based on one or more of: a peak signal energy in a sub-band, an average signal-to-noise ratio in the sub-band, and a peak signal-to-noise ratio in the sub-band.

4. The system according to claim 1, characterized in that The sub-band classification criteria are based on one or more of: scene characteristics, relative target velocity, azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

5. The system according to claim 1, wherein: The first type of sub-band processed by the first type of sub-band processor includes a sub-band containing frequencies lower than a first frequency threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band containing frequencies higher than a second frequency threshold corresponding to a predetermined long-range target, the third type of sub-band processed by the third type of sub-band processor includes a sub-band containing frequencies between the first frequency threshold and the second frequency threshold corresponding to a predetermined medium-range target, and the sub-band classification criteria include one or more of a center frequency of the sub-band and a bandwidth of the sub-band.

6. The system according to claim 1, wherein: The first type of sub-band processed by the first type of sub-band processor includes a sub-band containing a peak energy higher than a first energy threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band containing a peak energy lower than a second energy threshold corresponding to a predetermined long-range target, and the third type of sub-band processed by the third type of sub-band processor includes a sub-band containing a peak energy between the first energy threshold and the second energy threshold corresponding to a predetermined medium-range target.

7. The system according to claim 1, characterized in that The first type of sub-band processed by the first type of sub-band processor includes a sub-band including an average SNR higher than a first SNR threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band including an average SNR lower than a second SNR threshold corresponding to a predetermined long-range target, and the third type of sub-band processed by the third type of sub-band processor includes a sub-band including an average SNR threshold between the first SNR threshold and the second SNR threshold corresponding to a predetermined medium-range target.

8. A method in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, comprising: receiving range-dependent time-domain baseband signal samples; Separating the baseband signal samples into subbands in the time domain; classifying the sub-bands into a plurality of sub-band types based on a sub-band classification standard, selecting subband processing parameters for the subbands in the time domain and the frequency domain based on the plurality of subband types; and The subband is processed in the time domain and the frequency domain using the selected subband processing parameters, wherein: The bandwidth and center frequency combination of each subband comprises a first subband processing parameter, and processing the subband in the time domain comprises: generating a block of time domain samples in a block sampler, wherein an integration time of the block sampler includes a second subband processing parameter based on a target detection probability associated with each subband type; and A discrete Fourier transform (DFT) is performed on the time domain sample block using a DFT processor to generate subbands in the frequency domain, wherein a length of the DFT includes a third subband processing parameter based on a range resolution associated with each subband type, and a frequency of the DFT includes a fourth subband processing parameter based on an angular resolution associated with each subband type.

9. The method according to claim 8, characterized in that Processing the subbands in the frequency domain comprises: filtering each frequency domain subband in the frequency domain processor using a digital filter, wherein a length of the digital filter includes a fifth subband processing parameter based on the subband type; and A peak search processor is used to search for a signal peak, a signal-to-noise ratio (SNR) peak, or an average SNR above a threshold SNR in each frequency domain subband.

10. The method according to claim 8, characterized in that The sub-band classification criteria is based on one or more of: a peak signal energy in a sub-band, an average signal-to-noise ratio in the sub-band, and a peak signal-to-noise ratio in the sub-band.

11. The method according to claim 8, characterized in that The sub-band classification criteria are based on one or more of: scene characteristics, relative target velocity, azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

12. The method according to claim 8, characterized in that The first type of sub-band processed by the first type of sub-band processor includes a sub-band containing frequencies lower than a first frequency threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band containing frequencies higher than a second frequency threshold corresponding to a predetermined long-range target, the third type of sub-band processed by the third type of sub-band processor includes a sub-band containing frequencies between the first frequency threshold and the second frequency threshold corresponding to a predetermined medium-range target, and the sub-band classification criteria include one or more of a center frequency of the sub-band and a bandwidth of the sub-band.

13. The method according to claim 8, characterized in that The first type of sub-band processed by the first type of sub-band processor includes a sub-band containing a peak energy higher than a first energy threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band containing a peak energy lower than a second energy threshold corresponding to a predetermined long-range target, and the third type of sub-band processed by the third type of sub-band processor includes a sub-band containing a peak energy between the first energy threshold and the second energy threshold corresponding to a predetermined medium-range target.

14. The method according to claim 8, characterized in that The first type of sub-band processed by the first type of sub-band processor includes a sub-band including an average SNR higher than a first SNR threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processor includes a sub-band including an average SNR lower than a second SNR threshold corresponding to a predetermined long-range target, and the third type of sub-band processed by the third type of sub-band processor includes a sub-band including an average SNR threshold between the first SNR threshold and the second SNR threshold corresponding to a predetermined medium-range target.

15. A system comprising: A time domain processing subsystem for: receiving range-related time domain baseband signal samples in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, preconditioning the samples, and combining the preconditioned samples into a time domain sample block; a discrete Fourier transform (DFT) processor coupled to the time-domain processing subsystem for performing a DFT to convert the blocks of time-domain samples into frequency-domain subbands of a baseband signal; as well as a frequency domain processing subsystem coupled to the DFT processor and the time domain processing subsystem to generate subbands in the frequency domain, classify the subbands into a plurality of subband types based on a subband classification criterion, and process each subband using a subband processing parameter based on the subband type, wherein The time domain processing subsystem comprises: a time domain preprocessor for preconditioning the range-related baseband signal samples; and a sample block generator coupled to the preprocessor and the frequency domain processing subsystem to combine the preconditioned samples into the time domain sample blocks, wherein an integration time of the sample block generator includes a first subband processing parameter based on a detection probability associated with each subband type.

16. The system of claim 15, wherein: The length of the DFT includes a second subband processing parameter based on a range resolution associated with each subband type, and the frequency of the DFT includes a third subband processing parameter based on an angular resolution associated with each subband type.

17. The system of claim 15, wherein: The frequency domain processing subsystem comprises: a subband generator for generating subbands in the frequency domain, wherein the bandwidth and center frequency combination of each subband comprises a fourth subband processing parameter; a subband discriminator coupled to the subband generator and the sample block generator for classifying the subbands according to types and selecting subband processing parameters for each subband in the frequency domain based on the subband types; and A plurality of frequency domain processing chains are coupled to the subband generator and the subband discriminator for adjusting each subband, detecting a signal peak and a signal-to-noise ratio (SNR) peak and an average signal-to-noise ratio in each subband, and performing post-processing on each subband.

18. The system of claim 17, wherein: Each frequency domain processing chain of the plurality of frequency domain processing chains comprises: a frequency domain processor for filtering each subband based on the subband type, wherein a length of a digital filter in the frequency domain processor includes a fifth subband processing parameter based on a bandwidth associated with each subband type; a peak search processor coupled to the frequency domain processor to selectively search for energy peaks based on the subband type; and A frequency domain post-processor is used to selectively filter points in the LIDAR point cloud to remove noise.

19. The system of claim 15, wherein: The sub-band classification criteria is based on one or more of: a peak signal energy in a sub-band, an average signal-to-noise ratio in the sub-band, and a peak signal-to-noise ratio in the sub-band.

20. The system of claim 15, wherein: The sub-band classification criteria are based on one or more of the following: scene characteristics, relative target velocity, azimuth scan angle, altitude scan angle, and predicted targets based on previous frames.

21. The system of claim 15, wherein: The first type of sub-band processed by the first type of sub-band processing chain includes a sub-band containing frequencies below a first frequency threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processing chain includes a sub-band containing frequencies above a second frequency threshold corresponding to a predetermined long-range target, the third type of sub-band processed by the third type of sub-band processing chain includes a sub-band containing frequencies between the first frequency threshold and the second frequency threshold corresponding to a predetermined medium-range target, and the sub-band classification criteria include one or more of a center frequency of the sub-band and a bandwidth of the sub-band.

22. The system of claim 15, wherein: The first type of sub-band processed by the first type of sub-band processing chain includes a sub-band containing a peak energy higher than a first energy threshold corresponding to a predetermined short-range target, the second type of sub-band processed by the second type of sub-band processing chain includes a sub-band containing a peak energy lower than a second energy threshold corresponding to a predetermined long-range target, and the third type of sub-band processed by the third type of sub-band processing chain includes a sub-band containing a peak energy between the first energy threshold and the second energy threshold corresponding to a predetermined medium-range target.

23. The system of claim 15, wherein: The first type of sub-bands processed by the first type of sub-band processing chain include sub-bands including average SNRs above a first SNR threshold corresponding to a predetermined short-range target, the second type of sub-bands processed by the second type of sub-band processing chain include sub-bands including average SNRs below a second SNR threshold corresponding to a predetermined long-range target, and the third type of sub-bands processed by the third type of sub-band processing chain include sub-bands including average SNR thresholds between the first SNR threshold and the second SNR threshold corresponding to a predetermined medium-range target.

24. A method comprising: receiving, in a time domain processing subsystem, range-related time domain baseband signal samples in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system; combining the samples into blocks of samples in the time domain using a block of samples generator; transforming the block of samples from the time domain to the frequency domain using a discrete Fourier transform (DFT) in a DFT processor; generating subbands in the frequency domain using a subband generator; classifying the subband into a plurality of subband types based on a subband classification criterion using a subband discriminator; selecting subband processing parameters based on the subband type; as well as Each frequency subband is processed on the corresponding subband using the selected subband processing parameters, where Processing each frequency subband involves: selectively filtering each subband according to the subband type, wherein a length of a digital filter in a frequency domain processor includes a first subband processing parameter based on a bandwidth associated with each subband type; selectively searching for an energy peak or a signal-to-noise ratio (SNR) peak in a frequency subband according to a subband type; and Points in the LIDAR point cloud are selectively filtered to remove noise.

25. The method of claim 24, wherein: The integration time of the sample block generator includes a second subband processing parameter based on the detection probability associated with each subband type, the length of the DFT includes a third subband processing parameter based on the range resolution associated with each subband type, the periodicity (frequency) of the DFT includes a fourth subband processing parameter based on the angular resolution associated with each subband type, and the bandwidth and center frequency combination of each subband includes a fifth subband processing parameter.

26. The method of claim 24, wherein: The subband classification criteria include one or more of the following: peak signal energy in the subband, average signal-to-noise ratio in the subband, and peak signal-to-noise ratio in the subband; and the subband classification criteria include one or more of the following: scene characteristics, relative target speed, azimuth scanning angle, altitude scanning angle and predicted target based on previous frames.

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