Selection techniques for multiple return signals in frequency-modulated continuous-wave LIDAR systems

Abstract

A method for selecting return signals in a LIDAR (Light Detection and Ranging) system includes thresholding a frequency domain waveform and identifying a plurality of peaks corresponding to a plurality of targets that exceed a threshold level, applying a principal peak selection to the frequency domain waveform to identify a principal peak from the plurality of peaks, and then applying a secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the principal peak and identifying a secondary peak from the plurality of peaks that is outside the guard band.

Description

Related Applications

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 17 / 703,876, filed March 24, 2022, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] The present disclosure relates to LIDAR (Light Detection and Ranging) systems. Summary of the Invention [Problem to be solved by the invention]

[0003] Conventional frequency-modulated continuous wave (FMCW) LIDAR systems have several sources of noise and phase impairments, including laser phase noise, circuit phase noise, flicker noise, drift due to temperature and weather conditions, and chirp rate offset. These impairments can make it difficult to detect secondary peaks (peak signals that occur in addition to the main peak), increase false detections and range / velocity biases, and increase errors in estimated target range and velocity. [Means for solving the problem]

[0004] DETAILED DESCRIPTION OF THE INVENTION Aspects of the present invention, namely, LIDAR systems and methods that facilitate selection of multiple return signals or peaks, are described below. One aspect of the present invention is a LIDAR (Light Detection and Ranging) system comprising: a light beam source for transmitting a light beam to a target; a photodetector for receiving return light from the target; The following process: The signal obtained by processing the light beam and the return light A threshold is set on the frequency domain waveform, and peaks corresponding to the targets that exceed a threshold level are identified; applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak, to identify a secondary peak from the plurality of peaks outside the guard band; calculating distance and velocity information corresponding to the primary peak and the secondary peak; a signal processing circuit that executes the above; This is a LIDAR system equipped with:

[0005] Another aspect of the present invention is a method for selecting multiple return signals in a LIDAR (Light Detection and Ranging) system, comprising the steps of: Obtained by signal processing of a light beam transmitted to a target and return light reflected from said target. A threshold is set on the frequency domain waveform, and peaks exceeding the threshold level are identified from among multiple peaks corresponding to multiple targets. applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak, to identify a secondary peak from the plurality of peaks outside the guard band; calculating distance and velocity information corresponding to the primary peak and the secondary peak; This is a method for doing this.

[0006] Another aspect of the present invention is a LIDAR (Light Detection and Ranging) system comprising: a light beam source for transmitting a light beam to a target; a photodetector for receiving return light from the target; a signal processing circuit including a memory; The instructions stored in the memory, when executed, cause the signal processing circuitry to perform the following processes: The signal obtained by processing the light beam and the return light A threshold is set on the frequency domain waveform, and peaks corresponding to the targets that exceed a threshold level are identified; applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak, to identify a secondary peak from the plurality of peaks outside the guard band; calculating distance and velocity information corresponding to the primary peak and the secondary peak; The distance and velocity information is reflected in a point cloud. The LIDAR system is configured to: [Brief explanation of the drawings]

[0007] To clarify various aspects of the present invention, reference is made to the drawings that are referenced in the detailed description that follows, in which like reference numerals refer to like elements.

[0008] [Figure 1] FIG. 1 is an illustration of a LIDAR system according to an embodiment of the present disclosure. [Figure 2] FIG. 1 is a time-frequency diagram illustrating how LIDAR waveforms are detected and processed according to an embodiment of the present disclosure.

[0009] [Figure 3] FIG. 1 is a block diagram illustrating a LIDAR system according to an embodiment of the present disclosure.

[0010] [Figure 4A] FIG. 1 illustrates a frequency domain waveform including true target and noise events, according to an embodiment of the present disclosure. [Figure 4B] FIG. 1 illustrates a frequency domain waveform including true target and noise events, according to an embodiment of the present disclosure. [Figure 4C] FIG. 1 illustrates a frequency domain waveform including true target and noise events, according to an embodiment of the present disclosure.

[0011] [Figure 5]FIG. 1 illustrates a method for automatically adjusting a detection threshold according to an embodiment of the present disclosure.

[0012] [Figure 6A] FIG. 1 illustrates a technique for selecting multiple peaks using a threshold index, according to an embodiment of the present disclosure. [Figure 6B] FIG. 1 illustrates a technique for selecting multiple peaks using a peak selection index, according to an embodiment of the present disclosure.

[0013] [Figure 7] FIG. 10 illustrates an example of an additional peak selection technique, according to an embodiment of the present disclosure.

[0014] [Figure 8] FIG. 10 illustrates an example of a guard band for avoiding duplicate detection of the same target, according to an embodiment of the present disclosure.

[0015] [Figure 9] FIG. 2 illustrates an example of a static guard band according to an embodiment of the present disclosure.

[0016] [Figure 10] FIG. 2 illustrates an example of a dynamic guard band according to an embodiment of the present disclosure.

[0017] [Figure 11] FIG. 1 illustrates an example of a roll-off peak estimator according to an embodiment of the present disclosure.

[0018] [Figure 12] FIG. 1 illustrates an example of a segment-based peak selection approach, according to an embodiment of the present disclosure.

[0019] [Figure 13] FIG. 10 illustrates an example of a multi-pass algorithm used to select three peaks, according to an embodiment of the present disclosure.

[0020] [Figure 14]FIG. 10 illustrates an example of a subband peak selection process, according to an embodiment of the present disclosure.

[0021] [Figure 15] FIG. 10 illustrates the characteristics of a subband peak selection algorithm according to an embodiment of the present disclosure.

[0022] [Figure 16] FIG. 10 illustrates an example of another subband peak selection algorithm according to an embodiment of the present disclosure.

[0023] [Figure 17] FIG. 1 is a flow diagram illustrating a method for selecting multiple peaks in a LIDAR system according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0024] Various LIDAR systems and methods for detecting multiple return signals according to embodiments of the present invention are described below. The LIDAR systems of the present invention may be implemented in any sensing market, including, but not limited to, transportation, manufacturing, metrology, medical, virtual reality, augmented reality (AR), and security systems. Additionally, the LIDAR systems described in the present invention may be implemented as part of the front end of a frequency modulated continuous wave (FMCW) device that aids in the spatial awareness of autonomous driving assistance systems and autonomous vehicles.

[0025] FIG. 1 illustrates a LIDAR system 100 according to one embodiment. LIDAR system 100 may include any one or more of several components, but may include fewer or additional components than those shown in FIG. 1 . In some embodiments, one or more of the components shown in FIG. 1 may be implemented on a photonics chip. As shown in FIG. 1 , LIDAR system 100 includes an optical circuit 101 implemented on a photonics chip. Optical circuit 101 includes a combination of active and passive optical components. In some examples, the active optical components include one or more optical amplifiers, one or more photodetectors, and the like, with light beams of different wavelengths.

[0026] The free-space optics 115 includes one or more optical waveguides for transmitting optical signals and for routing and manipulating the optical signals to appropriate input / output ports of the active optical circuit. The free-space optics 115 also includes one or more optical components such as taps, wavelength division multiplexers (WDMs), splitters / combiners, polarizing beam splitters (PBSs), collimators, and couplers. In one embodiment, the free-space optics 115 includes components for converting polarization states and directing received polarized light to a photodetector, for example, using a PBS. The free-space optics 115 may also include a diffractive element for deflecting light beams having different frequencies at different angles along an axis (e.g., a fast axis). Although some embodiments are described with respect to a polarizing beam splitter (PBS), embodiments of the invention are not limited thereto and may include an optical circulator, a directional coupler, an MMI (multimode interference), a bistatic receiver, or similar components.

[0027] The LIDAR system 100 of this embodiment includes an optical scanner 102 having one or more scanning mirrors. These scanning mirrors are rotatable along an axis (e.g., a slow axis) orthogonal or substantially orthogonal to the fast axis of the diffractive element to direct an optical signal that scans the environment according to a scanning pattern. For example, the scanning mirrors can be rotated by one or more galvanometers. Objects in the target environment scatter the incident light to generate a return optical beam or target return signal. The optical scanner 102 can also collect and return the return optical beam or target return signal to passive optical circuit components of the optical circuit 101. For example, the return optical beam is directed to a photodetector by a polarizing beam splitter. Note that the optical scanner 102 may include wave plates, lenses, anti-reflection coated optical windows, etc. in addition to mirrors and galvanometers.

[0028] The LIDAR system 100 is provided with a LIDAR controller 110 to control and support the optical circuit 101 and the optical scanner 102. The LIDAR controller 110 contains the processing equipment required for the LIDAR system 100. In one embodiment, the processing device is one or more general-purpose processing devices such as a microprocessor, a central processing unit, and in particular a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing another instruction set or a combination of instruction sets. Additionally, the processing device may be one or more of 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, or the like.

[0029] In one embodiment, the LIDAR controller 110 is provided with a signal processing unit 112, such as a DSP, which outputs a digital control signal for controlling the optical driver 103. The digital control signal is converted into an analog signal via the signal conversion unit 106. For example, the signal conversion unit 106 includes a digital-to-analog converter. The optical driver 103 provides drive signals to the active optical components of the optical circuit 101 to drive light sources such as lasers and amplifiers. In one embodiment, multiple optical drivers 103 and signal conversion units 106 may be provided to drive multiple light sources.

[0030] The LIDAR controller 110 is also configured to output digital control signals to the optical scanner 102. The motion controller 105 can control the galvanometers of the optical scanner 102 based on the control signals received from the LIDAR controller 110. Specifically, a digital-to-analog converter can be used to convert the coordinate routing information from the LIDAR controller 110 into signals that can be processed by the galvanometers of the optical scanner 102. In one embodiment, the motion controller 105 may also send information regarding the position or movement of components of the optical scanner 102 back to the LIDAR controller 110. Specifically, an analog-to-digital converter may be used to convert the information regarding the galvanometer position into a signal that the LIDAR controller 110 can process in turn.

[0031] The LIDAR controller 110 is further configured to analyze the input digital signals. In this regard, the LIDAR system 100 includes an optical receiver 104 for measuring one or more beams received by the optical circuit 101. Specifically, the optical receiver 104, which serves as a reference beam receiver, measures the signal strength (amplitude) of the reference beam from the active optical component and converts the signal from the reference beam receiver via an analog-to-digital converter into a signal that can be processed by the LIDAR controller 110. The target receiver, also referred to as optical receiver 104, measures an optical signal carrying information about the range and velocity of the target in the form of a beat frequency modulated optical signal. In this case, the reflected beam of the optical signal may be mixed with a second signal (local copy) of the local oscillator. The optical receiver 104 may be equipped with a high-speed analog-to-digital converter to convert the signal from the target receiver into a signal that can be processed by the LIDAR controller 110.

[0032] In some applications, the LIDAR system 100 may additionally include one or more imagers 108 configured to capture images of the environment, a global positioning system (GPS) 109 configured to provide the geographic location of the system, or other sensor inputs. The LIDAR system 100 may also include an image processor 114 that may be configured to receive images and geographic locations from the imager 108 and the global positioning system (GPS) 109, and to transmit the images and locations or related information to the LIDAR controller 110 or other systems connected to the LIDAR system 100.

[0033] In some embodiments, the LIDAR system 100 is configured to simultaneously measure distance and velocity in two dimensions using a non-degenerate optical source. This capability enables real-time, long-range measurements of distance, velocity, azimuth, and elevation of the surrounding environment.

[0034] In one embodiment, the scanning process begins with the optical driver 103 and the LIDAR controller 110. The LIDAR controller 110 instructs the optical driver 103 to modulate one or more light beams, respectively, and these modulation signals are transmitted through the passive optical circuitry of the optical circuit 101 to a collimator in the free-space optics 115. The collimator directs the modulation signals to the optical scanner 102, which scans the environment in a pre-programmed pattern defined by the motion controller 105. The optical circuit 101 may include a polarizing waveplate (PWP) that converts the polarization state of the light beams as they exit the optical circuit 101. By way of example, the polarizing waveplate may be a quarter waveplate or a half waveplate. A portion of the polarized light beam may be reflected back into the optical circuit 101. For example, a lens system or collimating system used in the LIDAR system 100 may have natural reflective properties or a reflective coating, which causes a portion of the light beam to be reflected back into the optical circuit 101.

[0035] The optical signal reflected from the environment is sent to the receiver (optical receiver 104) through the optical circuit 101. At this time, the polarization state of the light has been converted, so it is reflected by the polarizing beam splitter along with a portion of the polarized light that has reflected back to the optical circuit 101. As a result, the reflected optical signal does not return to the same optical fiber or waveguide as the light source, but is reflected to different optical receivers. These signals interfere with each other, generating a mixed (combined) signal. Each beam signal returning from the target produces a time-shifted waveform, and the time phase difference between these two waveforms generates a beat frequency that is measured by the optical receiver (photodetector). The combined signal is then reflected back to the optical receiver 104.

[0036] The analog signal received by the optical receiver 104 is converted into a digital signal by an ADC (analog-to-digital converter), which is then sent to the LIDAR control device 110. A signal processing unit 112 of the device receives the digital signals and processes them. In one embodiment, the signal processing unit 112 receives position data from the motion controller 105 and a galvanometer (not shown) and image data from the image processor 114. This enables the signal processing unit 112 to generate a 3D point cloud with information about the distance and velocity of points in the environment as the optical scanner 102 scans additional points. The signal processing unit 112 may also overlay the 3D point cloud with image data to determine the speed and distance of surrounding objects. The LIDAR controller 110 may also process satellite-based navigation position data to provide accurate global position information.

[0037] 2 is a time-frequency diagram 200 of an FMCW scanning signal 201 that can be used in one embodiment by a LIDAR system, such as LIDAR system 100, to scan a target environment. FM The scanning signal 201, labeled (t), has a chirp bandwidth Δf C and chirp period T C It is a sawtooth waveform (sawtooth "chirp") with The sawtooth inclination is k=(Δf C / T C ) Also shown in Figure 2 is a target return signal 202 (return signal) in one embodiment. FM The target return signal 202, denoted (t-Δt), is a 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. This round-trip time is given by Δt=2R / v, where R is the range of the target and v is the speed of light, c, which is the velocity of the light beam. Therefore, the range R of the target can 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 R(t) is generated. Beat frequency Δf R (t) is linearly related to the time delay Δt by the sawtooth slope k. That is, Δf R (t) = kΔt. Since the target distance R is proportional to Δt, the target distance R is R = (c / 2)(Δf R (t) / k), that is, the distance R can be calculated as the beat frequency Δf R It has a linear relationship with (t). Beat frequency Δf R (t) is generated as an analog signal, for example, in optical receiver 104 of LIDAR system 100. This beat frequency is digitized, for example, by an analog-to-digital converter (ADC) in signal conditioning unit 107 of LIDAR system 100. The digitized beat frequency signal is then digitally processed in a signal processing unit (e.g., signal processing unit 112) in LIDAR system 100. However, it should be noted that the target return signal 202 typically contains a frequency offset (Doppler shift) if the target has a relative velocity with respect to the LIDAR system 100. For simplicity and ease of illustration, the Doppler shift is not shown in FIG. 2 because it is detected separately and used to correct the frequency of the return signal. It is also important to note that the sampling frequency of the ADC is determined by the highest beat frequency that the system can handle without aliasing. Generally, the highest frequency that can be handled is half the sampling frequency (i.e., the "Nyquist limit"). For example, but not by way of limitation, if the sampling frequency of the ADC is 1 GHz, then the highest beat frequency that can be handled without aliasing (Δf Rmax ) is 500 MHz. This limit is the system's maximum target range, R max =(c / 2)(Δf Rmax / k), which can be adjusted by changing the sawtooth inclination k. In one example, the data samples from the ADC may be continuous, but the subsequent digital processing described below may be divided into "time segments" that are associated with a predetermined periodicity of the LIDAR system 100. For example, but not by way of limitation, the time segments may be divided into "time segments" that are equal to or longer than the chirp period T C or the number of azimuthal rotations made by the optical scanner described above.

[0038] 3 is a block diagram of an exemplary LIDAR system according to an embodiment of the present disclosure. The system includes a beam source 301, such as an FMCW laser source. The target arm of the system includes multiple optical components (e.g., lenses and filters) 305 through which a scan signal 303 passes before reaching a target 307. A return signal 309 reflected from the target 307 is directed to a photodetector 311. From the photodetector 311, a digitally sampled target signal 316 is sent via a target ADC 315 to a digital signal processor (DSP) 317, which then sends it to a point cloud 329. In some embodiments, the DSP 317 can mitigate signal degradation and condition the signal for the peak detector 339. The DSP 317 can include a front-end processor 3330, which includes a time-domain processor 331, a block sampler 333 (to generate block samples for Fourier transform), a time-to-frequency converter 335, and a frequency-domain processor 337. The peak detector 339 can receive the frequency-domain waveform and identify frequency bins that are most likely to correspond to true targets.

[0039] 4A-4C show multiple frequency domain waveforms including true target and noise events according to an embodiment of the present disclosure. As shown in Figure 4A, the frequency-domain waveform containing the true target 403 can be corrupted by electrical interference 401, internal reflections that can create false targets, and various types of noise events 405, including shot noise, thermal noise, quantization noise, and phase noise. These noise sources are often inconsistent across frequency, which can affect detection accuracy. The role of the peak detector 339 is to accurately detect the true target 403 with a high probability of detection while keeping false positives low.

[0040] Figure 4B shows a frequency domain waveform without noise calibration or correction, and Figure 4C shows the same frequency domain waveform with noise calibration, in accordance with an embodiment of the present disclosure. In the waveform shown in Figure 4B, the noise event 405 has higher energy than the true target 403.

[0041] In the embodiment shown in Figure 4C, a confidence measure for the noise level 407 is calculated by comparing the frequency domain waveform with the estimated noise level 407. This makes the peak detector less susceptible to noise events. In Figure 4C, the true target 403 exhibits a higher confidence than the noise event 405. As an exemplary confidence measure, in some embodiments, the signal-to-noise ratio (SNR) is used.

[0042] 5 illustrates a method for automatically adjusting the detection threshold according to an embodiment of the present disclosure. In this embodiment, a strong target 501, a false positive 503, and a weak target 505 are each detected. However, only the strong target 501 exceeds a valid threshold value 509. In this embodiment, the location of the weak target 505 is estimated based on previous detections and point cloud data processing (target identification, tracking, etc.), and based on the estimated location of the weak target, the threshold is adjusted to favor detections within the predicted target band 507. Thus, the threshold is lowered within the predicted target band 507 to allow the weak target 505 to be detected, even if it has a confidence index similar to that of the false positive 503.

[0043] 6A and 6B illustrate a technique for selecting multiple peaks using a threshold index and a peak selection index, according to an embodiment of the present disclosure. In Fig. 6A, a threshold index M2(f) is applied to the frequency-domain waveform to identify peaks that exceed a threshold M(f). In Fig. 6B, a peak selection index M1(f) is applied to the frequencies above the threshold identified in Fig. 6A.

[0044] In one embodiment, the threshold metric used is SNR (signal to noise ratio) and the peak selection metric is intensity (signal strength). In the illustrated embodiment, SNR is used to remove all frequencies below a threshold MT(f), and then the peak with the highest intensity among the frequencies that pass thresholding is selected as the main peak (primary peak) 603. After selecting the main peak 603, the same or a different peak selection index can be used to select secondary peaks 601. For example, after thresholding using SNR, the main peak can be selected based on intensity, while thresholding using SNR can select secondary peaks based on SNR. For the third or more returns, all frequencies corresponding to the peaks already selected can be excluded from the search, and the search for secondary peaks can be performed for all frequencies outside the guard band 605 of the main peak 603 to avoid detecting side lobes of the main peak 603.

[0045] In one embodiment, the process of peak selection can be formulated as the following optimization, which uses different confidence metrics, M2(f) for thresholding and M1(f) for peak selection: TIFF0007719314000001.tif1346

[0046] Under the condition that M2(f)>MT(f), MT(f) is the required threshold. Similarly, the calculation of multiple peaks can be determined based on the same criteria: TIFF0007719314000002.tif1346

[0047] Under the condition M2(f)>MT(f), f~P1 indicates that peak detection is performed at all frequencies except the frequency belonging to the main peak P1. In practice, P1 can be defined based on a guard band, as detailed below.

[0048] 7 illustrates an example of another peak selection technique according to an embodiment of the present disclosure. In this embodiment, a primary peak 701 is identified, and there are three additional peaks that can be selected. In one embodiment, the secondary peak 705, the strongest peak with the second or smaller peak selection index magnitude, is selected regardless of its location (frequency). In this peak selection technique, the return signal is detected based on the value of the peak selection index.

[0049] In another embodiment, the highest frequency peak 707 among the detected frequencies above a given threshold can be selected as the secondary peak. This peak corresponds to the furthest peak detected from the sensor. This peak selection technique is biased to prioritize detection of targets that are farther away, allowing for priority detection of true targets even in the presence of fog or other scattered targets (e.g., chain link fences). When selecting the furthest peak, the search can begin backward from the highest frequency detection and stop at the first peak detected, since that peak corresponds to the highest frequency detection outside of the primary peak.

[0050] In yet another embodiment, the lowest frequency peak 701 among the detected frequencies above a given threshold can be selected as the secondary peak. This peak corresponds to the closest peak detected from the sensor. This peak selection technique is biased to prioritize detection of targets closest to the sensor and is useful for detecting occlusions or obstacles in the sensor window or other nearby objects. When selecting the closest peak, the search can begin with the lowest frequency detection and stop at the first peak detected, since that peak corresponds to the lowest frequency detection outside of the primary peak.

[0051] 8 illustrates an example of guard bands to avoid multiple detections of the same target, according to an embodiment of the present disclosure. In this embodiment, a strong target 801 contains multiple large sidelobes with larger amplitudes than a weak target 803. Therefore, a minimum guard band must be placed around the strong target 801 to ensure that the secondary peak that maximizes the peak selection index is detected as an independent target and not simply a sidelobe of the strong target. In this embodiment, a semi-guard band 805 is placed to the right of the strong target 801 to prevent the strong target's high-intensity side lobes from being selected as secondary peaks. Such an approach is useful when highly reflective targets, such as highly reflective road signs, generate large side lobes.

[0052] 9 illustrates an example of a static guard band, according to an embodiment of the present disclosure. In this embodiment, a primary peak 901 is identified, and a static guard band 905 is used to ignore detections around the primary peak 901 when searching for a secondary peak 903. Setting the guard band too small may not perform well for strong targets across a wide frequency range. On the other hand, setting the guard band too wide may result in poor resolution between closely spaced targets.

[0053] 10 illustrates an example of a dynamic guard band, where the bandwidth of the guard band can be adjusted based on peak characteristics such as SNR, intensity, or frequency, according to an embodiment of the present disclosure. 10 uses a predicted peak roll-off 1003 that models how amplitude rolls off as you move away from a main peak 1001. In this embodiment, a guard band 1005 is set around the main peak 1001, which is defined as the frequency band where the predicted roll-off exceeds a peak detection threshold. If a detected signal strength (amplitude) exceeds the predicted roll-off 1003, it may be treated as an independent detection, like peak 1007, even if it is within the guard band 1005. This allows for higher resolution detection of peaks that are close together in frequency.

[0054] 11 illustrates an example of a roll-off peak estimator according to an embodiment of the present disclosure. In this example, the predicted peak roll-off value is determined based on a Gaussian shape with bandwidths of 5, 10, and 20 MHz. In some embodiments, other distributions may be used that depend on the strength and frequency of the detected target.

[0055] 12 illustrates an example of a segment-based peak selection technique according to an embodiment of the present disclosure. The techniques described above may require multiple passes over the frequency waveform to select peaks. For example, one pass (including iterative processing) may select a primary peak, a second pass may select a secondary peak, and so on. This method requires storing the frequency-domain waveform throughout the peak selection process: in fact, the intensity, SNR, and other metrics used in peak selection must be stored for every frequency bin, as well as state variables such as previously selected peaks and guard bands. To reduce memory usage, the frequency-domain waveform can be divided into segments and only one representative value stored for each segment. Figure 12 shows an example of a frequency-domain waveform with N bins divided into N / M segments of length M, where only the highest detected value within each segment is stored. Increasing M can reduce memory usage, but the need to set guard bands on a segment-by-segment basis can limit the accuracy (resolution) of peak selection. For example, if the main peak is detected in the 10th segment, the 9th and 11th segments can be ignored when searching for secondary peaks.

[0056] In one embodiment, the technique described in Figure 12 functions as a downsampling process, where thresholding, main peak selection, and secondary peak selection are performed on a subset of downsampled frequencies (frequency sets), which correspond to the highest frequencies within each segment.

[0057] FIG. 13 shows an example of a multi-pass algorithm used to select three peaks, according to an embodiment of the present disclosure. In this example, one return signal is selected by performing each pass over the entire band and finding the maximum. After selecting the main peak 1301, a guard band is applied around it to exclude peak 1301'. The remaining bins are then used for the next pass. In the second pass, peak 1303 is selected as the secondary peak, and a guard band is similarly applied to exclude peak 1303'. In the third pass, peak 1305 is selected as the tertiary peak. In such an embodiment, if there are P return signals, P passes will be required, and each pass may introduce a delay in the output since the full band of data must be kept in memory for each pass. An example of pseudocode for this algorithm is shown below: present_input = full_band; % initialize input with full-band for i = 1 : kReturnCount [peak(i), bins(i)] = peak_picker(present_input); present_input = remove_guard_band(present_input); end

[0058] 14 shows an example of a subband peak selection process according to an embodiment of the present disclosure. In this embodiment, the entire band is divided into N subbands, and the frequency of each subband is processed sequentially, and this is repeated for all subsets (all subbands). The multi-pass algorithm described in FIG. 13 can be applied to each subband sequentially. In one embodiment, once the processing of the last subband is complete, a final peak for the entire band is determined. The processing of each subband uses a guard band 1401 consisting of the bins of that subband plus some bins from the previous subband. For example, when processing the second subband (SB2), SB2 and the guard band 1401 are used. The size of the guard band 1401 depends on the number of return signals and the size of the guard band. This technique avoids multiple passes over the entire band, reducing buffer memory usage, and can support implementations where subbands are available at a point (a mechanism whereby processing can be done incrementally on a subband-by-subband basis without all data being available at once). An example of pseudocode for this algorithm is shown below: present_input = sub_band(1, :); % initialize input with first sub-band for i = 1 : kSubBandsCount [peaks, bins] = multiple_pass_peak_picker(present_input); next_input = sub_band(i+1, :); % get next sub_band present_input = update_input(next_input, present_input, peaks, bins); end

[0059] In one embodiment, the technique described in Figure 14 avoids multiple passes over the entire band, reducing buffer memory usage and supporting implementations where subbands are available point-by-point. If N bins are divided into M subbands and P peaks are selected, then only M + (P - 1) x G bins need to be stored in memory (a small memory size is sufficient), where G is the size of the guard band (number of bins). In contrast, the full-band algorithm requires all N bins to be stored in memory. Smaller values of M, P, and G result in greater memory savings, while larger values increase memory usage.

[0060] The technique described in Figure 14 allows for a single pass through the entire frequency-domain waveform to detect all peaks. In one embodiment, the frequency-domain waveform can be divided into subbands and multiple passes can be performed at the subband level to apply thresholds and select primary and secondary peaks. Once a peak is selected at a subband level, that subband is considered processed and the next subband is selected, saving memory by discarding the processed subband. While multiple passes may be performed at the subband level, a single pass through the entire frequency-domain waveform allows for the selection of all peaks.

[0061] 15 illustrates features of a subband peak selection algorithm according to an embodiment of the present disclosure. In this embodiment, subband 1 is searched to identify a primary peak 1501, a secondary peak 1503, and a tertiary peak 1505. Meanwhile, peak 1513 is initially ignored because it falls within the full guard band (FGB) of primary peak 1501. Similarly, peak 1515 is also initially ignored because it falls within the FGB of secondary peak 1503. Next, as the process moves to subband 2, a new main peak 1511 is identified. Because the value of peak 1511 is greater than the value of 1501, peak 1511 becomes the new main peak, and peak 1501 falls within the guard band of the new main peak 1511 and is therefore ignored. With peak 1501 removed, peak 1513, which was initially ignored, returns to the list of possible secondary peaks. Furthermore, because the value of peak 1513 is greater than the value of 1503, peak 1513 becomes the new secondary peak. Because peak 1503 falls within the guard band of peak 1513, it is now removed. Extending the same logic, peak 1515 is selected as the new tertiary peak. In order to restore the peaks 1513 and 1515 that were initially ignored in this way, it is necessary to retain (store in memory) information for the guard bands (P-1) of the two peaks in the previous subband.

[0062] For example, if we select three peaks (P=3), with N=1024, M=64, and G=16, the memory requirements for this process are: 64 + 2 × 16 = 96 This is a significant memory savings compared to full bandwidth processing, which requires 1024 segments. The specific memory savings in this example are: 1024 - 96 = 928 This becomes:

[0063] FIG. 16 shows another example of a subband peak selection process according to an embodiment of the present disclosure. The process in this embodiment accepts one sample bin as input and updates peak information. The process begins by initializing peak value buffers and peak index (position information) buffers according to the number of peaks required (e.g., three buffers for three peaks). The top of the buffer stores the most recent (current) peak value, and the bottom stores previous values. In this example, the buffer size is equal to half the guard band (HGB half guard band), and the peaks are sorted in the order P1>P2>P3.

[0064] For each sample, the magnitude (signal strength) of the new input is compared to the current peak value. If the new input is smaller than all three peaks, nothing is done. If the new input is smaller than P1 and P2 but larger than P3, the top of P3's buffer is updated with the new input if the new value is outside the guard bands of P1 or P2. If the new input is less than P1 but greater than P2 and P3, then if the new value is outside the guard band of P1, the top of P2's buffer is updated with the new input, and the top of P3's buffer is updated with the bottom of P2's buffer. If the new input is larger than all three peaks, the top of P1's buffer is updated with the new input, the top of P2's buffer is updated with the bottom of P1's buffer, the top of P3's buffer is updated with the bottom of P2's buffer, etc. Each buffer is then pushed down one sample and the next input is analyzed.

[0065] Further memory savings can be achieved by the process disclosed in Figure 16. For example, if P peaks are selected from N bins and the size of the guard band is G, the memory buffer required is 2P x G / 2 = PG. In contrast, the full-band process needs to store all N bins, so the memory savings is N - PG. For example, when selecting three peaks (P=3), the memory uses a buffer of six half guard bands (HGB) or three full guard bands (FGB). If N=1024 and G=16, the process disclosed in Figure 16 requires the following memory amount: 3 × 16 = 48 This saves memory by 1024 - 48 = 976 This becomes:

[0066] FIG. 17 is a flow diagram illustrating a method for selecting multiple peaks in a LIDAR system according to an embodiment of the present disclosure. The method begins by thresholding the frequency domain waveform in operation 1701. As previously mentioned, this thresholding may be performed by applying an SNR threshold, an intensity threshold, or other threshold.

[0067] In operation 1703, a main peak selection is performed for the frequencies that pass the threshold set in operation 1701. This peak selection can be performed based on SNR values, intensity values, or other indicators.

[0068] In operation 1705, a main peak is identified based on the main peak selection.

[0069] In operation 1707, a secondary peak selection is performed, which may be based on SNR values, intensity values, or other metrics, and is applied to frequencies outside the guard band of the primary peak identified in operation 1705.

[0070] In operation 1709, a secondary peak is identified based on a secondary peak selection, which, in various embodiments, may be identified based on the highest intensity peak outside the main peak, the lowest frequency peak outside the main peak, or the highest frequency peak outside the main peak.

[0071] In operation 1711, range and velocity information is calculated for each detected peak. Each identified peak corresponds to a target or object. Thus, the techniques disclosed herein enable the system to calculate range and velocity information (as well as other information such as reflectivity) for multiple targets or objects. In some embodiments, the range and velocity information can be reflected in a point cloud.

[0072] In some embodiments, if more than one peak needs to be identified, the process can continue to identify additional peaks, applying the same or different peak selection techniques as described above. Thus, the techniques disclosed herein can be used to detect multiple targets and calculate range and velocity data for any number of targets. In some embodiments, the range and velocity data for each target can be reflected in the point cloud.

[0073] The various operations and methods described herein may, in some embodiments, be performed using the signal processing unit 112, the signal conversion unit 106, or the signal conditioning unit 107 shown in Figure 1. Additionally, various optical components, optical fiber transmission delays, and other structural components may be implemented as the optical circuit 101 shown in Figure 1.

[0074] In the above description, several specific examples of specific systems, components, methods, etc. are shown to facilitate understanding of the embodiments of the present invention. However, those skilled in the art may practice the present invention without these specific examples. Also, details of well-known components and methods may be omitted or shown in simple block diagram form to facilitate understanding of the present invention. Therefore, the disclosed content is merely exemplary, and one example may differ from other examples, but is considered to be within the scope of the present invention.

[0075] The use of the phrase "one embodiment" or "an embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase "in one embodiment" or "an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0076] The term "connected" and its derivatives are used to indicate that two or more elements interact with each other. These connected elements may or may not be in direct physical or electrical contact.

[0077] Although the operations of the methods described herein are shown in a particular order, the order of the operations of each method may be changed, certain operations may be performed in reverse order, or at least some operations may be performed simultaneously with other operations. Different operations may be directed or sub-operations may be performed intermittently or alternately.

[0078] The above-described description of the illustrative embodiments of the invention (including those described in the Abstract) is not intended to be detailed or exhaustive, nor is it intended to limit the invention to the specific forms disclosed. While specific embodiments of and examples for the invention are described herein for illustrative purposes, various equivalent modifications will occur to those skilled in the art. 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 word "example" or "exemplary" is intended to illustrate concepts in a concrete manner. The term "or" as used herein is intended to be interpreted as an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, the phrase "X contains A or B" refers to any of the natural inclusive permutations: if X contains A, if X contains B, or if X contains both A and B, then the condition "X contains A or B" is satisfied in any of the above cases. Furthermore, the articles "a" and "an," as used in this specification and the appended claims, unless otherwise specified, shall be construed to mean "one or more" unless the singular form is clear from the context. Furthermore, when terms such as "first," "second," "third," and "fourth" are used in this specification, these terms are used as identifiers to distinguish between different elements and do not necessarily indicate an order according to the numerical designation.

Claims

1. 1. A LIDAR (Light Detection and Ranging) system comprising: a light beam source for transmitting a light beam to a target; a photodetector for receiving return light from the target; The following process: A threshold is set for a frequency domain waveform obtained by signal processing of the light beam and the return light, and a peak exceeding the threshold level is identified from among a plurality of peaks corresponding to a plurality of the targets; applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak to identify a secondary peak from the plurality of peaks outside the guard band; Calculating distance and velocity information corresponding to the primary peak and the secondary peak; a signal processing circuit that executes the above; A LIDAR system comprising:

2. 10. The system of claim 1, 1. A LIDAR system, wherein thresholding the frequency domain waveform is performed by applying a signal-to-noise ratio (SNR) threshold, and wherein the primary peak selection and secondary peak selection are performed by applying a signal intensity threshold.

3. 10. The system of claim 1, 1. A LIDAR system, wherein the guard band is a fixed frequency region set around the dominant peak in the frequency domain waveform.

4. 10. The system of claim 1, The secondary peak selection includes identifying the secondary peaks that lie within the guard band and exceed an expected peak roll-off value, and The predicted peak roll-off value is a distribution based on the signal strength and frequency of the main peak for a LIDAR system.

5. 10. The system of claim 1, The secondary peak selection includes selecting a peak with the highest signal strength, a peak with the highest frequency, or a peak with the lowest frequency from the plurality of peaks that are outside the guard band.

6. 10. The system of claim 1, The signal processing circuit further includes: Dividing the frequency domain waveform into a plurality of segments; The threshold value is set for each of the plurality of segments; applying the primary peak selection and the secondary peak selection to the maximum value of each of the plurality of segments; 1. A LIDAR system configured to:

7. 10. The system of claim 1, The signal processing circuit further includes: Dividing the frequency domain waveform into a plurality of subbands; setting the threshold on a first subband of the plurality of subbands to identify a plurality of peaks that exceed a threshold level; applying the dominant peak selection in a first pass to the first subband to identify a dominant peak of the first subband from the plurality of peaks; applying the secondary peak selection in a second pass on the first subband to identify a secondary peak for the first subband from the plurality of peaks; performing the thresholding, the main peak selection, and the secondary peak selection on the remaining subbands to identify all peaks in one pass over the entire frequency domain waveform; 1. A LIDAR system configured to:

8. 8. The system of claim 7, The signal processing circuit further includes: storing the first subband's dominant peak and the first subband's secondary peak in a memory; storing all of the plurality of peaks that exceed a threshold level within a guard band of two peaks in the first subband in a memory corresponding to a second subband; performing the threshold setting, the main peak selection, and the secondary peak selection on the second subband to identify a main peak and a secondary peak of the second subband; comparing the primary peak and the secondary peak of the first subband with the primary peak and the secondary peak of the second subband to identify new primary peaks and new secondary peaks in the first subband and the second subband; 1. A LIDAR system configured to:

9. 10. The system of claim 1, The signal processing circuit further includes: storing the primary peak and the secondary peak in a peak value buffer in memory; Analyzing new inputs to the frequency domain waveform to identify new peaks that exceed a threshold level; If the new peak has a value greater than the values of the main peak and the secondary peak and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a new main peak; If the new peak has a value greater than the value of the secondary peak and less than the value of the main peak, and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a new secondary peak; If the new peak has a value smaller than the values of the main peak and the secondary peak and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a tertiary peak; 1. A LIDAR system configured to:

10. 1. A method for selecting multiple return signals in a LIDAR (Light Detection and Ranging) system, comprising the steps of: A threshold is set for a frequency domain waveform obtained by signal processing of a light beam transmitted to a target and return light reflected from the target, and a peak exceeding the threshold level is identified from among a plurality of peaks corresponding to a plurality of targets; applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak to identify a secondary peak from the plurality of peaks outside the guard band; Calculating distance and velocity information corresponding to the primary peak and the secondary peak; How to do it.

11. 11. The method of claim 10, The method, wherein thresholding the frequency domain waveform is performed by applying a signal-to-noise ratio (SNR) threshold, and the main peak selection and the secondary peak selection are performed by applying a signal intensity threshold.

12. 11. The method of claim 10, The method of claim 1, wherein the guard band is a fixed frequency region set around a dominant peak in the frequency domain waveform.

13. 11. The method of claim 10, The secondary peak selection includes identifying secondary peaks that lie within the guard band and that exceed an expected peak roll-off value, and The method wherein the predicted peak roll-off values are a distribution based on the signal strength and frequency of the main peak.

14. 11. The method of claim 10, The method, wherein the secondary peak selection includes selecting the peak with the highest signal strength, the peak with the highest frequency, or the peak with the lowest frequency from the plurality of peaks outside the guard band.

15. 11. The method of claim 10, In addition, the following processes: Dividing the frequency domain waveform into a plurality of segments; The threshold value is set for each of the plurality of segments; applying the primary peak selection and the secondary peak selection to the maximum value of each of the plurality of segments; How to perform.

16. 11. The method of claim 10, In addition, the following processes: Dividing the frequency domain waveform into a plurality of subbands; setting the threshold on a first subband of the plurality of subbands to identify a plurality of peaks that exceed a threshold level; applying the dominant peak selection in a first pass to the first subband to identify a dominant peak of the first subband from the plurality of peaks; applying the secondary peak selection in a second pass on the first subband to identify a secondary peak for the first subband from the plurality of peaks; performing the thresholding, the main peak selection, and the secondary peak selection on the remaining subbands to identify all peaks in one pass over the entire frequency domain waveform; How to perform.

17. 17. The method of claim 16, In addition, the following processes: storing the first subband's dominant peak and the first subband's secondary peak in a memory; storing all of the plurality of peaks that exceed a threshold level within a guard band of two peaks in the first subband in a memory corresponding to a second subband; performing the threshold setting, the main peak selection, and the secondary peak selection on the second subband to identify a main peak and a secondary peak of the second subband; comparing the primary peak and the secondary peak of the first subband with the primary peak and the secondary peak of the second subband to identify new primary peaks and new secondary peaks in the first subband and the second subband; How to perform.

18. 11. The method of claim 10, In addition, the following processes: storing the primary peak and the secondary peak in a peak value buffer in memory; Analyzing new inputs to the frequency domain waveform to identify new peaks that exceed a threshold level; If the new peak has a value greater than the values of the main peak and the secondary peak and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a new main peak; If the new peak has a value greater than the value of the secondary peak and less than the value of the main peak, and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a new secondary peak; If the new peak has a value smaller than the values of the main peak and the secondary peak and is outside the guard bands of the main peak and the secondary peak, update the peak value buffer with the new peak as a tertiary peak; How to perform.

19. 1. A LIDAR (Light Detection and Ranging) system comprising: a light beam source for transmitting a light beam to a target; a photodetector for receiving return light from the target; a signal processing circuit including a memory; The instructions stored in the memory, when executed, cause the signal processing circuitry to perform the following processes: A threshold is set for a frequency domain waveform obtained by signal processing of the light beam and the return light, and a peak exceeding the threshold level is identified from among a plurality of peaks corresponding to a plurality of targets; applying a dominant peak selection to the frequency domain waveform to identify a dominant peak from the plurality of peaks; applying secondary peak selection to a portion of the frequency domain waveform outside a guard band corresponding to the primary peak to identify a secondary peak from the plurality of peaks outside the guard band; Calculating distance and velocity information corresponding to the primary peak and the secondary peak; The distance and velocity information is reflected in a point cloud.

1. A LIDAR system configured to:

20. 20. The system of claim 19, 1. A LIDAR system, wherein thresholding the frequency domain waveform is performed by applying a signal-to-noise ratio (SNR) threshold, and wherein the primary peak selection and secondary peak selection are performed by applying a signal intensity threshold.

Images