Patent Publication Number: US-2022229163-A1

Title: Techniques to associate peaks in multi-target scenarios in coherent lidar systems

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/339,763 filed on Jun. 4, 2021, which claims priority to U.S. Provisional Patent Application No. 63/104,372 filed on Oct. 22, 2020, the entire contents of which are incorporated herein by reference in their entirety. 
    
    
     FIELD 
     The present disclosure is related to light detection and ranging (LIDAR) systems in general, and more particularly to peak association of multi-target scenarios in coherent LIDAR systems. 
     BACKGROUND 
     Frequency-Modulated Continuous-Wave (FMCW) LIDAR systems use tunable, infrared lasers for frequency-chirped illumination of targets, and coherent receivers for detection of backscattered or reflected light from the targets that are combined with a local copy of the transmitted signal. Mixing the local copy with the return signal, delayed by the round-trip time to the target and back, generates signals at the receiver with frequencies that are proportional to the distance to each target in the field of view of the system. An up sweep of frequency and a down sweep of frequency may be used to detect a range and velocity of a detected target. However, when a scene includes multiple targets, the issue of associating the peaks corresponding to each target arises. 
     SUMMARY 
     The present disclosure describes examples of systems and methods for associating peaks in multi-target scenarios in a LIDAR system. 
     In one embodiment, a method includes transmitting optical beams including different frequency chirps towards targets in a field of view of a light detection and ranging (LIDAR) system and receiving return signals based on reflections from the targets, wherein each return signal includes a different frequency. The method further includes generating a baseband signal in a frequency domain based on the return signals, the baseband signal including a first set of peaks each associated with a different up-chirp frequency and a second set of peaks each associated with a different down-chirp frequency. The method includes generating one or more metrics associated with each of the first set of peaks and each of the second set of peaks and identifying the targets based on a pairing of each peak of the first set of peaks with a peak of the second set of peaks using the one or metrics. 
     In some embodiments, the one or more metrics include similar computed peak shapes between each peak of the first set of peaks and the second set of peaks. In some embodiments, the similar computed peak shapes include a correlator output for each of the first set of peaks compared with each of the second set of peaks. In some embodiments, the one or more metrics further include at least one of peak intensity, peak width, ego-velocity, and raw peak frequencies. 
     In some embodiments, the method includes combining the one or more metrics for each the first set of peaks and the second set of peaks into a combined metric and identifying the targets based on the combined metric for each of the first set of peaks and the second set of peaks. In some embodiments, combining the one or more metrics into a combined metric includes weighting each of the one or more metrics to generate weighted metrics and summing one or more of the weighted metrics to generate a weighted sum of the one or more metrics. 
     In some embodiments, identifying the targets includes performing an optimization algorithm to minimize a cost function associated with the pairing of each of the first set of peaks and the second set of peaks, the cost function corresponding to a sum of the one or more metrics for the pairing of each of the first set of peaks and the second set of peaks. 
     In some embodiments, the method further includes identifying neighboring data points of each of the first set of peaks and the second set of peaks and identifying the targets further based on the neighboring data points of each of the first set of peaks and the second set of peaks. 
     In some embodiments, each of the neighboring data points includes one or more of data points neighboring each of the first set of peaks and the second set of peaks in an azimuthal space, an elevation space, a three-dimensional space, or in a temporal space. In some embodiments, determining that a first number of peaks in the first set of peaks is different from a second number of peaks in the second set of peaks and, in response to determining that the first number of peaks does not match the second number of peaks, identifying the targets based on an extra peak being detected by either the up-chirp frequencies or the down-chirp frequencies, or identifying the targets based on a missed detection of either the up-chirp frequencies or the down-chirp frequencies. 
     In one embodiment, a light detection and ranging (LIDAR) system includes an optical scanner to transmit optical beams each including different frequency chirps toward targets in a field of view of the LIDAR system and receive return signals from reflections of optical beams from the targets wherein each return signal from the return signals includes a different frequency. The LIDAR system further includes an optical processing system coupled to the optical scanner to generate an electrical signal from the return signals, the electrical signal including frequencies corresponding to LIDAR target ranges and a signal processing system coupled to the optical processing system. The signal processing system includes a processing device and a memory to store instructions that, when executed by the processing device, cause the LIDAR system to generate a baseband signal in a frequency domain based on the electrical signal generated from the return signals, the baseband signal including a first set of peaks each associated with a different up-chirp frequency and a second set of peaks each associated with a different down-chirp frequency. The instructions further cause the processing device to generate one or more metrics associated with each of the first set of peaks and each of the second set of peaks of the baseband signal and identify the targets based on a pairing of each peak of the first set of peaks with a peak of the second set of peaks or the baseband signal using the one or metrics. 
     In one embodiment, a LIDAR system includes an optical scanner to transmit optical beams each including different frequency chirps toward one or more targets in a field of view of the LIDAR system and receive return signals from reflections of the optical beams from the one or more targets, wherein each return signal from the return signals includes a different frequency. The LIDAR system further includes an optical processing system coupled to the optical scanner to generate an electrical signal from the return signals, the electrical signal including frequencies corresponding to LIDAR target ranges, and a signal processing system coupled to the optical processing system. The signal processing system includes a processing device and a memory to store instructions that, when executed by the processing device, cause the LIDAR system to identify a first set of peaks of the electrical signal, each peak of the first set of peaks being associated with a different up-chirp frequency, and a second set of peaks of the electrical signal, each peak of the second set of peaks being associated with a different down-chirp frequency. The instructions further cause the processing device to determine one or more peak metrics for each of the first set of peaks and the second set of peaks, generate peak pairs by associating each peak of the first set of peaks with a peak of the second set of peaks based on the one or more peak metrics, and identify the one or more targets based on the peak pairs. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of various examples, reference is now made to the following detailed description taken in connection with the accompanying drawings in which like identifiers correspond to like elements: 
         FIG. 1  is a block diagram illustrating an example LIDAR system according to the present disclosure; 
         FIG. 2  is a time-frequency diagram illustrating one example of LIDAR waveforms according to the present disclosure; 
         FIG. 3A  is a block diagram illustrating an example LIDAR system according to the present disclosure; 
         FIG. 3B  is a block diagram illustrating an electro-optical optical system according to the present disclosure; 
         FIG. 4  is a block diagram of an example signal processing system according to the present disclosure; 
         FIG. 5  is a signal magnitude-frequency diagram illustrating signal peaks for multiple targets according to the present disclosure; 
         FIG. 6  is a detailed example of a magnitude-frequency diagram illustrating similar shapes of peaks for a target used for peak association according to the present disclosure; 
         FIG. 7  is a block diagram of an example signal processing system for peak association using computed metrics of each peak according to the present disclosure; 
         FIG. 8  is a block diagram illustrating correlator metric generation according to the present disclosure; 
         FIG. 9  is a flowchart illustrating a method for signal peak association according to the present disclosure; and 
         FIG. 10  is a block diagram of an example signal processing system according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure describes various examples of LIDAR systems and methods for associating peaks in multi-target scenarios in a LIDAR system. According to some embodiments, the described LIDAR system may be implemented in any sensing market, such as, but not limited to, transportation, manufacturing, metrology, medical, virtual reality, augmented reality, and security systems. According to some embodiments, the described LIDAR system can be implemented as part of a front-end of frequency modulated continuous-wave (FMCW) device that assists with spatial awareness for automated driver assist systems, or self-driving vehicles. 
     LIDAR systems described by the embodiments herein include coherent scan technology to detect a signal returned from a target to generate a coherent heterodyne signal, from which range and velocity information of the target may be extracted. A signal, or multiple signals, may include an up-sweep of frequency (up-chirp) and a down-sweep of frequency (down-chirp), either from a single optical source or from separate optical source (i.e., one source with an up-sweep and one source with a down-sweep). Accordingly, two different frequency peaks, one for the up-chirp and one for the down-chirp, may be associated with a target and can be used to determine target range and velocity. In a scene with multiple targets, there may be several frequency peak pairs that are to be associated together; one pair for each target. However, since there may be many possible peak pair combinations generated from a multiple target scene, it can be difficult to optimally associate the correct peak pairs together. Using the techniques described herein, embodiments of the present invention can, among other things, address the issues described above by generating metrics for each of the peaks and possible peak pairs and performing an association algorithm based on the generated metrics. Several metrics for each peak and possible peak pair may be combined for performing the association algorithm. Accordingly, using several metrics generated for each of the possible peak pairs, the peaks pairs can be optimally associated together. 
       FIG. 1  illustrates a LIDAR system  100  according to example implementations of the present disclosure. The LIDAR system  100  includes one or more of each of a number of components, but may include fewer or additional components than shown in  FIG. 1 . One or more of the components depicted in  FIG. 1  can be implemented on a photonics chip, according to some embodiments. The optical circuits  101  may include a combination of active optical components and passive optical components. Active optical components may generate, amplify, and/or detect optical signals and the like. In some examples, the active optical component includes optical beams at different wavelengths, and includes one or more optical amplifiers, one or more optical detectors, or the like. 
     Free space optics  115  may include one or more optical waveguides to carry optical signals, and route and manipulate optical signals to appropriate input/output ports of the active optical circuit. The free space optics  115  may also include one or more optical components such as taps, wavelength division multiplexers (WDM), splitters/combiners, polarization beam splitters (PBS), collimators, couplers, non-reciprocal elements such as Faraday rotator or the like. In some examples, the free space optics  115  may include components to transform the polarization state and direct received polarized light to optical detectors using a PBS, for example. The free space optics  115  may further include a diffractive element to deflect optical beams having different frequencies at different angles along an axis (e.g., a fast-axis). 
     In some examples, the LIDAR system  100  includes an optical scanner  102  that includes one or more scanning mirrors that are rotatable along an axis (e.g., a slow-axis) that is orthogonal or substantially orthogonal to the fast-axis of the diffractive element to steer optical signals to scan an environment according to a scanning pattern. For instance, the scanning mirrors may be rotatable by one or more galvanometers. The optical scanner  102  also collects light incident upon any objects in the environment into a return optical beam that is returned to the passive optical circuit component of the optical circuits  101 . For example, the return optical beam may be directed to an optical detector by a polarization beam splitter. In addition to the mirrors and galvanometers, the optical scanner  102  may include components such as a quarter-wave plate, lens, anti-reflective coated window or the like. 
     To control and support the optical circuits  101  and optical scanner  102 , the LIDAR system  100  includes LIDAR control systems  110 . The LIDAR control systems  110  may include a processing device such as signal processing unit  112 . In some examples, signal processing unit  112  may be one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, signal processing unit  112  may be complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Signal processing unit  112  may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. 
     In some examples, signal processing unit  112  is a digital signal processor (DSP). The LIDAR control systems  110  are configured to output digital control signals to control optical drivers  103 . In some examples, the digital control signals may be converted to analog signals through signal conversion unit  106 . For example, the signal conversion unit  106  may include a digital-to-analog converter. The optical drivers  103  may then provide drive signals to active optical components of optical circuits  101  to drive optical sources such as lasers and amplifiers. In some examples, several optical drivers  103  and signal conversion units  106  may be provided to drive multiple optical sources. 
     The LIDAR control systems  110  are also configured to output digital control signals for the optical scanner  102 . A motion control system  105  may control the galvanometers of the optical scanner  102  based on control signals received from the LIDAR control systems  110 . For example, a digital-to-analog converter may convert coordinate routing information from the LIDAR control systems  110  to signals interpretable by the galvanometers in the optical scanner  102 . In some examples, a motion control system  105  may also return information to the LIDAR control systems  110  about the position or operation of components of the optical scanner  102 . For example, an analog-to-digital converter may in turn convert information about the galvanometers&#39; position to a signal interpretable by the LIDAR control systems  110 . 
     The LIDAR control systems  110  are further configured to analyze incoming digital signals. In this regard, the LIDAR system  100  includes optical receivers  104  to measure one or more beams received by optical circuits  101 . For example, a reference beam receiver may measure the amplitude of a reference beam from the active optical component, and an analog-to-digital converter converts signals from the reference receiver to signals interpretable by the LIDAR control systems  110 . Target receivers measure the optical signal that carries information about the range and velocity of a target in the form of a beat frequency, modulated optical signal. The reflected beam may be mixed with a second signal from a local oscillator. The optical receivers  104  may include a high-speed analog-to-digital converter to convert signals from the target receiver to signals interpretable by the LIDAR control systems  110 . In some examples, the signals from the optical receivers  104  may be subject to signal conditioning by signal conditioning unit  107  prior to receipt by the LIDAR control systems  110 . For example, the signals from the optical receivers  104  may be provided to an operational amplifier for amplification of the received signals and the amplified signals may be provided to the LIDAR control systems  110 . 
     In some applications, the LIDAR system  100  may additionally include one or more imaging devices  108  configured to capture images of the environment, a global positioning system  109  configured to provide a geographic location of the system, or other sensor inputs. 
     The LIDAR system  100  may also include an image processing system  114 . The image processing system  114  can be configured to receive the images and geographic location, and send the images and location or information related thereto to the LIDAR control systems  110  or other systems connected to the LIDAR system  100 . 
     In operation according to some examples, the LIDAR system  100  is configured to use nondegenerate optical sources to simultaneously measure range and velocity across two dimensions. This capability allows for real-time, long range measurements of range, velocity, azimuth, and elevation of the surrounding environment. 
     In some examples, the scanning process begins with the optical drivers  103  and LIDAR control systems  110 . The LIDAR control systems  110  instruct the optical drivers  103  to independently modulate one or more optical beams, and these modulated signals propagate through the passive optical circuit to the collimator. The collimator directs the light at the optical scanning system that scans the environment over a preprogrammed pattern defined by the motion control system  105 . The optical circuits  101  may also include a polarization wave plate (PWP) to transform the polarization of the light as it leaves the optical circuits  101 . In some examples, the polarization wave plate may be a quarter-wave plate or a half-wave plate. A portion of the polarized light may also be reflected back to the optical circuits  101 . For example, lensing or collimating systems used in LIDAR system  100  may have natural reflective properties or a reflective coating to reflect a portion of the light back to the optical circuits  101 . 
     Optical signals reflected back from the environment pass through the optical circuits  101  to the receivers. Because the polarization of the light has been transformed, it may be reflected by a polarization beam splitter along with the portion of polarized light that was reflected back to the optical circuits  101 . Accordingly, rather than returning to the same fiber or waveguide as an optical source, the reflected light is reflected to separate optical receivers. These signals interfere with one another and generate a combined signal. Each beam signal that returns from the target produces a time-shifted waveform. The temporal phase difference between the two waveforms generates a beat frequency measured on the optical receivers (photodetectors). The combined signal can then be reflected to the optical receivers  104 . 
     The analog signals from the optical receivers  104  are converted to digital signals using ADCs. The digital signals are then sent to the LIDAR control systems  110 . A signal processing unit  112  may then receive the digital signals and interpret them. In some embodiments, the signal processing unit  112  also receives position data from the motion control system  105  and galvanometers (not shown) as well as image data from the image processing system  114 . The signal processing unit  112  can then generate a 3D point cloud with information about range and velocity of points in the environment as the optical scanner  102  scans additional points. The signal processing unit  112  can also overlay a 3D point cloud data with the image data to determine velocity and distance of objects in the surrounding area. The system also processes the satellite-based navigation location data to provide a precise global location. 
       FIG. 2  is a time-frequency diagram  200  of an FMCW scanning signal  201  that can be used by a LIDAR system, such as system  100 , to scan a target environment according to some embodiments. In one example, the scanning waveform  201 , labeled as f FM (t), is a sawtooth waveform (sawtooth “chirp”) with a chirp bandwidth Δf C  and a chirp period T C . The slope of the sawtooth is given as k=(Δf C /T C ).  FIG. 2  also depicts target return signal  202  according to some embodiments. Target return signal  202 , labeled as f FM (t−Δt), is a time-delayed version of the scanning signal  201 , where Δt is the round trip time to and from a target illuminated by scanning signal  201 . The round trip time is given as Δt=2R/v, where R is the target range and v is the velocity of the optical beam, which is the speed of light c. The target range, R, can therefore be calculated as R=c(Δt/2). When the return signal  202  is optically mixed with the scanning signal, a range dependent difference frequency (“beat frequency”) Δf R (t) is generated. The beat frequency Δf R (t) is linearly related to the time delay Δt by the slope of the sawtooth k. 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 R (t)/k). That is, the range R is linearly related to the beat frequency Δf R (t). The beat frequency Δf R (t) can be generated, for example, as an analog signal in optical receivers  104  of system  100 . The beat frequency can then be digitized by an analog-to-digital converter (ADC), for example, in a signal conditioning unit such as signal conditioning unit  107  in LIDAR system  100 . The digitized beat frequency signal can then be digitally processed, for example, in a signal processing unit, such as signal processing unit  112  in system  100 . It should be noted that the target return signal  202  will, in general, also includes a frequency offset (Doppler shift) if the target has a velocity relative to the LIDAR system  100 . The Doppler shift can be determined separately, and used to correct the frequency of the return signal, so the Doppler shift is not shown in  FIG. 2  for simplicity and ease of explanation. It should also be noted that the sampling frequency of the ADC will determine the highest beat frequency that can be processed by the system without aliasing. In general, the highest frequency that can be processed is one-half of the sampling frequency (i.e., the “Nyquist limit”). In one example, and without limitation, if the sampling frequency of the ADC is 1 gigahertz, then the highest beat frequency that can be processed without aliasing (Δf Rmax ) is 500 megahertz. This limit in turn determines the maximum range of the system as 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 partitioned into “time segments” that can be associated with some periodicity in the LIDAR system  100 . In one example, and without limitation, a time segment might correspond to a predetermined number of chirp periods T, or a number of full rotations in azimuth by the optical scanner. It should be noted that while embodiments of the present disclosure may be used in conjunction with FMCW LiDAR, the disclosure is not limited to FMCW LiDAR and embodiments may be used with any other form of coherent LiDAR as well. 
       FIG. 3A  is a block diagram illustrating an example FMCW LIDAR system  300  according to the present disclosure. Example system  300  includes an optical scanner  301  to transmit an FMCW (frequency-modulated continuous wave) infrared (IR) optical beam  304  and to receive a return signal  313  from reflections of the optical beam  304  from targets such as target  312  in the field of view (FOV) of the optical scanner  301 . System  300  also includes an optical processing system  302  to generate a baseband signal  314  in the time domain from the return signal  313 , where the baseband signal  314  contains frequencies corresponding to LIDAR target ranges. Optical processing system  302  may include elements of free space optics  115 , optical circuits  101 , optical drivers  103  and optical receivers  104  in LIDAR system  100 . System  300  also includes a signal processing system  303  to measure energy of the baseband signal  314  in the frequency domain, to compare the energy to an estimate of LIDAR system noise, and to determine a likelihood that a signal peak in the frequency domain indicates a detected target. Signal processing system  303  may include elements of signal conversion unit  106 , signal conditioning unit  107 , LIDAR control systems  110  and signal processing unit  112  in LIDAR system  100 . 
       FIG. 3B  is a block diagram illustrating an example electro-optical system  350 . Electro-optical system  350  includes the optical scanner  301 , similar to the optical scanner  102  illustrated and described in relation to  FIG. 1 . Electro-optical system  350  also includes the optical processing system  302 , which as noted above, may include elements of free space optics  115 , optical circuits  101 , optical drivers  103 , and optical receivers  104  in LIDAR system  100 . 
     Electro-optical processing system  302  includes an optical source  305  to generate the frequency-modulated continuous-wave (FMCW) optical beam  304 . The optical beam  304  may be directed to an optical coupler  306  that is configured to couple the optical beam  304  to a polarization beam splitter (PBS)  307  and a sample  308  of the optical beam  304  to a photodetector (PD)  309 . The PBS  307  is configured to direct the optical beam  304 , because of its polarization, toward the optical scanner  301 . Optical scanner  301  is configured to scan a target environment with the optical beam  304 , through a range of azimuth and elevation angles covering the field of view (FOV)  310  of a LIDAR window  311  in an enclosure  320  of the optical system  350 . In  FIG. 3B , for ease of illustration, only the azimuth scan is illustrated. 
     As shown in  FIG. 3B , at one azimuth angle (or range of angles), the optical beam  304  passes through the LIDAR widow  311  and illuminates a target  312 . A return signal  313  from the target  312  passes through LIDAR window  311  and is directed by optical scanner  301  back to the PBS  307 . 
     The return signal  313 , which will have a different polarization than the optical beam  304  due to reflection from the target  312 , is directed by the PBS  307  to the photodetector (PD)  309 . In PD  309 , the return signal  313  is optically mixed with the local sample  308  of the optical beam  304  to generate a range-dependent baseband signal  314  in the time domain. The range-dependent baseband signal  314  is the frequency difference between the local sample  308  of the optical beam  304  and the return signal  313  versus time (i.e., Δf R (t)). 
       FIG. 4  is a detailed block diagram illustrating an example of the signal processing system  303 , which processes the baseband signal  314  according to some embodiments. As noted above, signal processing unit  303  may include elements of signal conversion unit  106 , signal conditioning unit  107 , LIDAR control systems  110  and signal processing unit  112  in LIDAR system  100 . 
     Signal processing system  303  includes an analog-to-digital converter (ADC)  401 , a time domain signal module  402 , a block sampler  403 , a discrete Fourier transform (DFT) module  404 , a frequency domain signal module  405 , and a peak search module  406 . The component blocks of signal processing system  303  may be implemented in hardware, firmware, software, or some combination of hardware, firmware and software. 
     In  FIG. 4 , the baseband signal  314 , which is a continuous analog signal in the time domain, is sampled by ADC  401  to generate a series of time domain samples  315 . The time domain samples  315  are processed by the time domain module  402 , which conditions the time domain samples  315  for further processing. For example, time domain module  402  may apply weighting or filtering to remove unwanted signal artifacts or to render the signal more tractable for subsequent processing. The output  316  of time domain module  402  is provided to block sampler  403 . Block sampler  403  groups the time domain samples  316  into groups of N samples  317  (where N is an integer greater than 1), which are provided to DFT module  404 . DFT module  404  transforms the groups of N time domain samples  317  into N frequency bins or subbands  318  in the frequency domain, covering the bandwidth of the baseband signal  314 . The N subbands  319  are provided to frequency domain module  405 , which conditions the subbands for further processing. For example, frequency domain module  405  may resample and/or average the subbands  319  for noise reduction. Frequency domain module  405  may also calculate signal statistics and system noise statistics. The processed subbands  319  are then provided to a peak search module  406  that searches for signal peaks representing detected targets in the FOV of the LIDAR system  300 . 
       FIG. 5  is an example of a signal magnitude-frequency diagram  500  illustrating signal peaks for multiple targets according to some embodiments. In some embodiments, an FMCW LIDAR system may generate an up-chirp and a down-chirp frequency modulation (also referred to herein as “up-sweep” and “down-sweep”, respectively) to scan a target environment and to determine range and velocity of targets within that environment. In one example, a single optical source may generate both the up-chirp and the down-chirp. In another example, the system may include an optical source to generate an optical beam that includes the up-chirp and a different optical source to generate an optical beam that includes the down-chirp. Using the returned signal and corresponding generated beat frequencies (i.e., peak frequencies) from the up-chirp and down-chirp, a signal processing system (e.g., signal processing unit  112  of  FIG. 1  and signal processing system  303 ) can determine both a range of a target and a velocity of the target. In some embodiments, to determine the range of the target (i.e., distance from the LIDAR system), the signal processing unit  112  may sum the two frequencies of the two peaks and then divide the sum by two. In some embodiments, to determine the velocity of the target, the signal processing unit  112  may divide the difference between the two peak frequencies by two. In some scenarios, as depicted in  FIG. 5 , there may arise situations in which multiple targets are in a scene resulting in multiple peak pairs. 
     As depicted in  FIG. 5 , from two targets in the field of view of the LIDAR system, the signal processing unit  112  may identify several peak frequencies (e.g., peak frequencies of the subbands  319 ). For example, the signal processing unit  112  may identify a first down-sweep target peak  520 A and a second down-sweep target peak  520 B from the down-sweep signal and a first up-sweep target peak  525 A and a second up-sweep target peak  525 B from the up-sweep signal. Accordingly, the signal processing unit  112  may match the peaks in one of several ways in which the peaks could possibly be matched. To correctly associate the pairs together, the signal processing unit  112  may calculate several metrics for each peak and each peak pair possibility, as described in more detail below with respect to  FIGS. 7-9 . For example, as illustrated by  FIG. 6 , the peaks corresponding to the same target may have very similar shapes and thus, the signal processing unit  112  may calculate a metric associated with peak shape and use the calculated metric to optimally associate the peaks of a target. However, as discussed below, the signal processing unit  112  may calculate many other metrics for the peaks and use the metrics to perform peak association. As depicted, the signal processing unit  112  may identify a first target pair  505  including the first down-sweep target peak  520 A and the first up-sweep target peak  525 A based on the calculated metrics (e.g., peak shape, intensity, etc.). Additionally, the signal processing unit  112  may identify a second target pair  510  including the second down-sweep target peak  520 B and the second up-sweep target peak  525 B based on the calculated metrics (e.g., peak shape, intensity, etc.). 
       FIG. 6  is a magnitude versus frequency diagram  600  illustrating up-sweep and down-sweep signal peaks for multiple targets. As illustrated, the subband frequency distribution (e.g., subbands  319 ) is associated with a respective up-sweep signal and down-sweep signal each having a corresponding peak associated with each target. The peaks of the up-sweep signal and the down-sweep signal that correspond to the same target may have similar properties. For instance, the first peak of the up-sweep  601  and the first peak of the down-sweep  602  have similar properties, including shape and intensity, and are offset from one another in the frequency spectrum. Similarly, the second peak of the upsweep  603  and the second peak of the down-sweep  604  have common properties, such as shape and intensity with a similar frequency offset. Accordingly, a signal processing system (e.g., signal processing unit  112  of  FIG. 1 ) may generate metrics for detected peaks based on characteristics of the peaks to be used to optimally perform a peak association. 
       FIG. 7  depicts a peak association system  700  for peak association optimization. Peak association system  700  may be included in signal processing system  112  of  FIG. 1  and may be the same or similar to peak search module  406  of  FIG. 4 . In one embodiment, a metrics computation module  705  receives N number of up-sweep peaks (peaks  701 A-N) and N number of down-sweep peaks (peaks  702 A-N), where N is a positive non-zero integer. The metrics computation module  705  may also receive several frequency bins proximate to the peaks  701 A-N and  702 A-N. In some embodiments, the metrics computation module  705  then generates one or more metrics  715  for each of the peaks  701 A-N and  702 A-N and peak pair combinations of the peaks  701 A-N and  702 A-N. The metrics computation module  705  may generate the one or more metrics  715  based on properties of the peaks  701 A-N and  702 A-N as well as the frequency bins that are proximate to the peaks. For example, the metrics computation module  705  may generate metrics  715  for the peaks  701 A-N and  702 A-N and possible peak pairs based on, but not limited to, the shape of the peaks, intensity of the peaks, peak width, raw frequencies of the peaks, SNR, reflectivity, ego velocity of the LIDAR system, and/or any other characteristics of the target, the peaks, and the LIDAR system. Accordingly, the metrics computation module  705  may generate several metrics  715  for each of the N×N number of possible peak pairs. 
     In one embodiment, the metrics calculation module  705  may take each of the individually calculated metrics  715  and combine them into a combined metric. For example, the metrics calculation module  705  may generate a weighted sum of the metrics  715  for each of the peaks or possible peak pairs. Each of the metrics  715  may be weighted in a manner corresponding to the associated relevance and or confidence of the metric. For example, metrics such as shape of the peak which provide for a high confidence for correct association may be weighted more than other metrics providing lower confidence scores. It should be noted that the individual metrics may be combined in any combination and in any manner to provide for a combined metric. In one example, the weights for each of the metrics may be static. In another example, the weights may be determined dynamically based on feedback from the peak association algorithm. In one embodiment, the weights may be determined by a machine learning model to optimize the performance of the peak association algorithm. 
     The metrics calculation module  705  may then provide the one or more generated metrics  715  to peak association module  710 . The peak association module  710  may optimize peak association based on an algorithm using the one or more metrics  715  as an input. In one example, the peak association module  710  may perform a greedy algorithm by first selecting the peak pair that has the highest correlation based on the metrics  715 , associate and remove that pair and then continue with the next highest correlation. In another example, a more complex algorithm may be performed (e.g., a Hungarian algorithm) to find the overall cost optimization based on the peak metrics  715 . 
     In some embodiments, the number of up-sweep peaks and down-sweep peaks may differ. In such a case, the metrics computation module  705  may generate NxM number of metrics  715 , wherein N is the number of up-sweep peaks and M is the number of down-sweep peaks, or vice versa. In some embodiments, the peak association module  710  may perform multiple algorithms to account for situations in which the number of peaks from the up-sweep is not the same as the number of peaks from the down-sweep. For example, the peak association module  710  may perform a first peak association algorithm when the number of peaks from the up-sweep and down-sweep are the same. However, two possible scenarios may occur in which the number of peaks are not the same. First, a target may be missed by one of the sweeps and therefore there is a mismatch due to the missed detection. In this case, the peak association module  710  may perform a second peak association algorithm to match all peaks with a pair except for a subset of the peaks (i.e., not all peaks are forced to match). In the second scenario, a false detection may occur causing an extra peak associated with the up-peak or the down-peak. In this case, the peak association module  710  may perform a third peak association algorithm to match all the peaks with a pair except for the extra, false alarm peak (i.e., weak peaks that are potential false alarm peaks could be excluded from the peak association algorithm). 
       FIG. 8  is an example correlator  800  for determining a correlation between the shapes of the peaks (e.g., cross-correlation) as performed by embodiments of the present disclosure. In some embodiments, the correlator  800  may be included in signal processing unit  112  of  FIG. 1 . In some embodiments, the correlator may be included in metrics computation module  705  of  FIG. 7 . As illustrated, each of the up-sweep peaks  810 A-N may be input into a correlator with each of the down-sweep peaks  820 A-N to generate a correlator value for each of the possible combinations of up-sweep and down-sweep peak pairs. Input into each correlator  810 A-(N×N) may be K number of samples (i.e., frequency bins) around each of the input peaks, where K is a positive, non-negative integer. In one example, the correlator may perform a convolution of the two peaks and K samples that are input into the correlator  810 A-(N×N) and produce 2K+1 number of correlation samples. The correlator  810 A-(N×N) may then select the maximum correlation sample from the 2K+1 correlation output as the output of the correlator  810 A-(N×N). Accordingly, a maximum correlation of each of the possible peak pairs is generated which can then be provided to the peak association module  710  described above in  FIG. 7 . 
       FIG. 9  is a flowchart illustrating a method  900  in a LIDAR system, such as LIDAR system  100  or LIDAR system  300 , of associating peaks in multi-target scenarios in a LIDAR system. 
     Method  900  begins at operation  902 , an optical scanner (e.g., optical scanner  301 ) transmits multiple optical beams, each optical beam including different up-chirp frequencies towards targets in a field of view of a LIDAR system. At operation  904 , the optical scanner (e.g., optical scanner  301 ) receives return signals based on reflections from the targets, each return signal including a different frequency. 
     At operation  906 , an optical processing system (e.g., optical processing system  302 ) generates a baseband signal in a frequency domain based on the return signals, the baseband signal including a first set of peaks each associated with a different up-chirp frequency and a second set of peaks each associated with a different down-chirp frequency. 
     At operation  908 , a metrics computation module (e.g., metrics computation module  705 ) of a signal processing system (e.g., signal processing system  303 ) generates one or more metrics associated with each of the first set of peaks and each of the second set of peaks. In some embodiments, the one or more metrics include similar computed peak shapes between each peak of the first plurality of peaks and the second plurality of peaks. In some embodiments, the one or metrics may include a correlation between each of the up-sweep peaks and each of the down-sweep peaks based on a similarity between peak shapes, peak intensity, peak widths, or any other characteristics of the peaks. The one or more metrics may also include external metrics such as ego/sensor velocity. In one example, the similarity between peak shapes may be determined by applying a correlator to generate a correlation value for each of the first plurality of peaks compared with each of the second plurality of peaks. 
     In some embodiments, the metrics computation module (e.g., metrics computation module  705 ) may combine the one or more metrics for each the first plurality of peaks and the second plurality of peaks into a combined metric and perform the peak association based on the combined metric for each of the first plurality of peaks and the second plurality of peaks. For example, the metrics computation module (e.g., metrics computation module  705 ) may weight each of the one or more metrics to generate weighted metrics and sum one or more of the weighted metrics to generate a weighted sum of the one or more metrics. 
     At operation  910 , a peak association module (e.g., peak association module  710 ) identifies the targets based on a pairing of each peak of the first set of peaks with a peak of the second set of peaks using the one or more metrics. To identify the targets from the first and second set of peaks, the peak association module (e.g., peak association module  710 ) may perform an optimization algorithm to minimize a cost function corresponding to a difference between matched peaks. In one example, the peak association module (e.g., peak association module  710 ) may identify a plurality of neighboring data points of each of the first plurality of peaks and the second plurality of peaks and perform the peak association further based on the plurality of neighboring data points of each of the first plurality of peaks and the second plurality of peaks. Each of the plurality of neighboring data points may include one or more of data points neighboring the peaks in an azimuthal space, an elevation space, a three-dimensional space, or in a temporal space. 
     The peak association module (e.g., peak association module  710 ) may further determine that a first number of the first plurality of peaks is different from a second number of the second plurality of peaks (e.g., missing peaks or extra peak detection). In some embodiments, the peak association module (e.g., peak association module  710 ) may perform variants of a peak association algorithm to account for missing or extra peaks. For example, in response to determining that the number of peaks in the first plurality of peaks does not match the number of peaks in the second set of peaks, the peak association module (e.g., peak association module  710 ) may perform the peak association based on a first algorithm associated with an extra peak being detected (e.g., extra peak detected by either the up-chirp or the down-chirp). In another example, in response to determining that a target detection has been missed by either the up-chirp or down-chirp, the peak association module (e.g., peak association module  710 ) may perform the peak association based on a second algorithm for missed detections. 
       FIG. 10  is a block diagram of a processing system  1000  (e.g., similar to signal processing system  303  illustrated and described above with respect to  FIG. 4 ) in a LIDAR system such as LIDAR system  100  or LIDAR system  300 . Processing system  1000  includes a processing device  1001 , which may be any type of general purpose processing device or special purpose processing device designed for use in the LIDAR system (also see, e.g., signal processing unit  112  in  FIG. 1 ). Processing device  1001  is coupled with a memory  1002 , which can be any type of non-transitory computer-readable medium (e.g., RAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic disk memory or optical disk memory) containing instructions that, when executed by processing device  1001  in the LIDAR system, cause the LIDAR system to perform the method described herein. In particular, memory  1002  includes instructions  1004  to transmit multiple optical beams, each optical beam including different up-chirp frequencies towards targets in a field of view of a LIDAR system; instruction  1006  to receive return signals based on reflections from the targets, each return signal including a different frequency; instruction  1008  to generate a baseband signal in a frequency domain based on the return signals, the baseband signal including a first set of peaks each associated with a different up-chirp frequency and a second set of peaks each associated with a different down-chirp frequency; instruction  1010  to generate one or more metrics associated with each of the first set of peaks and each of the second set of peaks; and instruction  1012  to identify each the targets based on a pairing of each peak of the first set of peaks with a peak of the second set of peaks using the one or more metrics. 
     The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a thorough understanding of several examples in the present disclosure. It will be apparent to one skilled in the art, however, that at least some examples of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram form in order to avoid unnecessarily obscuring the present disclosure. Thus, the specific details set forth are merely exemplary. Particular examples may vary from these exemplary details and still be contemplated to be within the scope of the present disclosure. 
     Any reference throughout this specification to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection with the examples are included in at least one example. Therefore, the appearances of the phrase “in one example” or “in an example” in various places throughout this specification are not necessarily all referring to the same example. 
     Although the operations of the methods herein are shown and described in a particular order, the order of the operations of each method may be altered so that certain operations may be performed in an inverse order or so that certain operation may be performed, at least in part, concurrently with other operations. Instructions or sub-operations of distinct operations may be performed in an intermittent or alternating manner. 
     The above description of illustrated implementations of the invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific implementations of, and examples for, the invention are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. The words “example” or “exemplary” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “example” or “exemplary” is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X includes A or B” is intended to mean any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then “X includes A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. as used herein are meant as labels to distinguish among different elements and may not necessarily have an ordinal meaning according to their numerical designation.