Technique for automatically adjusting detection threshold in FMCW-LIDAR systems

Abstract

A method for adjusting a detection threshold in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system includes: a. determining a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; b. determining a subset of frequencies within the frequency range for detecting a second target, the second target transmitting a signal within the subset of frequencies that is weaker than the first confidence threshold; c. adjusting the first confidence threshold to a second confidence threshold by the processor for detecting the second target within the subset of frequencies; and d. restoring the second confidence threshold to the first confidence threshold for detecting the first target outside the subset of frequencies.

Description

[Technical field]

[0001] This application claims priority under 35 U.S.C. §119(e) to U.S. patent application Ser. No. 17 / 339,737, filed June 4, 2021, and U.S. Provisional Patent Application Ser. No. 63 / 093,621, filed October 19, 2020, the entire contents of which are incorporated herein by reference.

[0002] The present invention relates generally to light detection and ranging (LIDAR) systems, and more particularly to techniques for adjusting the detection threshold in frequency modulated continuous wave (FMCW) LIDAR systems for target detection. [Background technology]

[0003] LIDAR systems, such as frequency modulated continuous wave (FMCW) LIDAR systems, use a tunable infrared laser for frequency chirp illumination of targets and a coherent receiver to detect the backscattered or reflected light from the targets, which is combined with a local copy of the transmitted signal at the coherent receiver. Mixing the local copy with the return signal delayed by the round trip time to the target produces a signal at the receiver with a frequency proportional to the distance to each target in the system's field of view. In such LIDAR systems, the use of low-power lasers is mandatory for human safety reasons. As a result, the signal strength reflected from objects is very weak. The detection distance and accuracy of a LIDAR system are functions of the S / N ratio (signal-to-noise ratio), but conventional countermeasures have not been able to reliably detect targets with weak reflected signals, nor have they been able to limit false detection of targets. Summary of the Invention [Means for solving the problem]

[0004] Hereinafter, embodiments of the present invention, that is, embodiments of the invention relating to a LIDAR system and a method thereof that automatically adjusts a detection threshold for target detection in order to solve the above problems, will be described.

[0005] One aspect of the invention is a light detection and ranging (LIDAR) system comprising: an optical scanner for transmitting a beam of light towards a target and receiving a return signal from the target; an optical processor coupled to the optical scanner for generating from the return signal a time domain baseband signal having a frequency dependent on the distance of the target; a signal processor connected to the optical processor, The signal processing device includes: A processor; and a memory operatively associated with the processor, the memory storing instructions that, when executed by the processor, cause the LIDAR system to perform the following operations: a. determine a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; b. determining a subset of frequencies within the frequency range for detecting a second target, the second target transmitting a signal within the subset of frequencies weaker than the first confidence threshold; c. adjusting the first confidence threshold to a second confidence threshold for detecting the second target within the subset of frequencies; and d. Changing the second confidence threshold back to the first confidence threshold for detecting a first target outside the subset of frequencies.

[0006] In the LIDAR system according to one aspect of the present invention, in order to determine the first reliability threshold, the processor further performs the following processing. a. determining a noise estimate over said frequency range; and b. Calculate the difference between the signal strength at each frequency and the noise estimate over the frequency range. In accordance with one aspect of the present invention, there is provided a LIDAR system comprising: The first confidence threshold is based on at least one of a signal to noise ratio or a likelihood ratio, which provides a confidence value for target detection. In a LIDAR system according to one aspect of the present invention, to determine a subset of frequencies in the frequency range for detecting the second target, the processor determines the subset of frequencies based on an estimated position of the second target. In a LIDAR system according to one aspect of the present invention, the processor further identifies previous detections of the second target in the time domain or frequency domain, and determines an estimated position of the second target based on the previous detections of the second target.

[0007] In a LIDAR system according to one aspect of the present invention, to determine an estimated position of the second target, the processor identifies point cloud information associated with the second target and determines an estimated position of the second target based on the point cloud information. In a LIDAR system according to one aspect of the invention, the subset of frequencies includes a predefined frequency range that corresponds to a position of the second target previously detected. In a LIDAR system according to one aspect of the present invention, the step of adjusting the first confidence threshold includes reducing, by the processor, the first confidence threshold to the second confidence threshold within a subset of the frequencies corresponding to an estimated location of the second target. In a LIDAR system according to one aspect of the present invention, the processor includes determining a subset of the frequencies that corresponds to the estimated position of the second target based on a confidence value associated with the estimated position of the second target. In the LIDAR system according to one aspect of the present invention, the processor further comprises: a. determining whether one or more previous detections of the second target include a weak detection signal; b. determining an estimated location of the second target; and c. adjusting the first confidence threshold if it is determined that one or more previous detections of the second target included a weak detection signal.

[0008] A method according to one embodiment of the present invention includes the following steps a to d. determining a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; determining, by a processor, a subset of frequencies within the frequency range for detecting a second target, the second target transmitting a signal within the subset of frequencies weaker than the first confidence threshold; c. adjusting, by the processor, the first confidence threshold to a second confidence threshold for detecting the second target within the subset of frequencies. d. Changing the second confidence threshold back to the first confidence threshold for detecting the first target outside the subset of frequencies.

[0009] According to one aspect of the present invention, a non-transitory computer readable medium stores instructions that, when executed by a processor, cause a LIDAR system to perform the following operations: a. determine a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; b. determining a subset of frequencies within the frequency range for detecting a second target, the second target transmitting a signal within the subset of frequencies weaker than the first confidence threshold; c. adjusting the first confidence threshold to a second confidence threshold for detecting the second target within the subset of frequencies; and d. Changing the second confidence threshold back to the first confidence threshold for detecting a first target outside the subset of frequencies. [Brief description of the drawings]

[0010] In order to clarify various aspects of the present invention, reference is made to the drawings which are referred to in the following detailed description (embodiments), in which like reference numerals refer to like elements.

[0011] [Figure 1] FIG. 1 is a block diagram illustrating a LIDAR system according to an embodiment of the present invention.

[0012] [Diagram 2] FIG. 2 is a time-frequency diagram illustrating an example LIDAR waveform according to an embodiment of the present invention.

[0013] [Figure 3A] FIG. 1 is a block diagram illustrating a LIDAR system according to an embodiment of the present invention.

[0014] [Figure 3B] FIG. 1 is a block diagram illustrating the electro-optical system of a LIDAR system according to an embodiment of the present invention.

[0015] [Figure 4] 1 is a block diagram showing a signal processing device according to an embodiment of the present invention;

[0016] [Diagram 5] FIG. 2 is a block diagram illustrating a signal processing scheme for peak detection with thresholding of estimated target positions in accordance with an embodiment of the present invention.

[0017] [Figure 6A] FIG. 2 is a signal strength-frequency diagram illustrating an example method of peak detection according to an embodiment of the present invention.

[0018] [Figure 6B]FIG. 13 shows a signal strength-frequency diagram for comparing the difference between the signal spectrum and the noise estimate, illustrating a noise estimate (confidence threshold) according to an embodiment of the present invention.

[0019] [Figure 7] FIG. 11 is a confidence index versus frequency diagram illustrating an example of how to adjust a confidence threshold for peak detection, according to an embodiment of the present invention.

[0020] [Figure 8] 4 is a flow chart illustrating a method of target detection according to an embodiment of the present invention.

[0021] [Figure 9] FIG. 2 is a block diagram showing a signal processing system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A LIDAR system and method according to an embodiment of the present invention, namely, a LIDAR system and method for automatically adjusting a detection threshold to improve target detection and reduce false positives, is described below. The LIDAR system of the embodiments of the present invention can be implemented in any sensing market, including, but not limited to, transportation, manufacturing, metrology, medical, virtual reality, augmented reality, and security systems. Additionally, the LIDAR system described in the embodiments can 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.

[0023] The LIDAR system of the embodiments described herein uses coherent scanning techniques to detect return signals from a target and generate a coherent heterodyne signal from which range and velocity information of the target can be obtained. Such signals can be converted into one or more frequency bins, with each bin containing the signal strength of the associated frequency within that bin. For example, target detection may correspond to a large signal strength (i.e., peak) in one or more frequency bins. In the LIDAR system of this embodiment, a detection threshold can be used to determine whether such peaks have sufficient signal strength to correspond to a detected target, and the detection threshold can be increased to reduce false target detections, or it can be set lower to increase the probability of detecting weak signals. Using techniques according to the present disclosure, embodiments of the present invention can address the above problems by reducing the detection threshold, particularly in a narrow frequency band near the estimated location of the target, thus increasing the detection probability for targets with weak return signals while maintaining a large detection threshold for the remaining frequency range to limit false positives.

[0024] FIG. 1 illustrates a LIDAR system 100 according to one embodiment. The LIDAR system 100 may include any one or more of numerous components, but may include fewer or additional components than those shown in FIG. 1. In one embodiment, one or more of the components shown in FIG. 1 may be implemented on a photonics chip. The optical circuit 101 includes a combination of active and passive optical components. The active optical components generate, amplify, and / or detect optical signals, etc. In some examples, the active optical components have light beams of different wavelengths and include one or more optical amplifiers, one or more photodetectors, etc.

[0025] The free-space optics 115 includes one or more optical waveguides for transmitting optical signals and for routing and manipulating the optical signals to the 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, couplers, and non-reciprocal elements such as Faraday rotators. In one embodiment, the free-space optics 115 includes components for converting the polarization state and directing the received polarized light to a photodetector, for example, using a PBS. The free-space optics 115 may also include a diffractive element that deflects light beams having different frequencies at different angles along an axis (e.g., the fast axis).

[0026] The LIDAR system 100 of this embodiment includes an optical scanner 102 having one or more scanning mirrors that are rotatable along an axis orthogonal or substantially orthogonal to the fast axis (e.g., a slow 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. The optical scanner 102 also collects the return beam aperture reflected by any object in the environment and directs it back to the passive optical circuit elements of the optical circuit 101. For example, the return optical beam is directed by a polarizing beam splitter to a photodetector. Note that the optical scanner 102 may include quarter wave plates, lenses, anti-reflective coated optical windows, etc. in addition to mirrors and galvanometers.

[0027] 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 includes a processing device, such as a signal processing unit 112. For example, the signal processing unit 112 may be one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or 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 other instruction sets or a combination of instruction sets. Additionally, the signal processing unit 112 may be one or more special purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like.

[0028] In one embodiment, a digital signal processor (DSP) is used as the signal processing unit 112. The LIDAR controller 110 is configured to output a digital control signal for controlling the optical driver 103. The digital control signal is converted to 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.

[0029] 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. In particular, 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. In particular, 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.

[0030] The LIDAR controller 110 is further configured to analyze the input digital signal. In this regard, the LIDAR system 100 is provided with an optical receiver 104 for measuring one or more beams received by the optical circuit 101. In particular, the optical receiver 104 as a reference beam receiver measures the amplitude of a reference beam from the active optical components and converts the signal from the reference beam receiver by an analog-to-digital converter into a signal that can be processed by the LIDAR controller 110. The target receiver as optical receiver 104 also 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. In one aspect, the signal from the optical receiver 104 may be subject to signal conditioning by the signal conditioning unit 107 before being received by the LIDAR controller 110. For example, the signal from the optical receiver 104 may be provided to an operational amplifier in the signal conditioning unit 107 for amplification of the return signal, and the amplified signal provided by the operational amplifier to the LIDAR controller 110.

[0031] In some applications, the LIDAR system 100 may additionally be provided with one or more imagers 108 configured to capture images of the environment, a global positioning system (GPS) 109 configured to provide a 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 Global Positioning System (GPS) 109, and to transmit the images and location or related information to the LIDAR controller 110 or other systems connected to the LIDAR system 100.

[0032] In operation according to some embodiments, the LIDAR system 100 is configured to simultaneously measure range and velocity in two dimensions using a non-degenerate optical source. This capability enables real-time, long-range measurements of range, velocity, azimuth, and elevation of the surrounding environment.

[0033] As an example, the scanning process is initiated by 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 circuit of the optical circuit 101 to the collimator of the free space optical system 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 be provided with a polarizing waveplate (PWP) that converts the polarization state of the light as it exits the optical circuit 101. For example, the polarizing waveplate may be a 1 / 4 waveplate or a 1 / 2 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 that causes a portion of the light beam to be reflected back into the optical circuit 101.

[0034] 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 was 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 and generate 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 produces a beat frequency that is measured by an optical receiver (photodetector). The mixed signal can then be reflected to the optical receiver 104 .

[0035] The analog signal received by the optical receiver 104 is converted to a digital signal by an ADC (analog-to-digital converter), which is then sent to the LIDAR controller 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 precise global position information.

[0036] 2 is a time-frequency diagram 200 of an FMCW scanning signal 201 that a LIDAR system, such as LIDAR system 100, can use to scan a target environment in one embodiment. 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 inclination of the sawtooth is k=(Δf C / T C ). Also shown in FIG. 2 is a target return signal 202 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 speed 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. Typically, the highest frequency that can be handled is half the sampling frequency (i.e., the "Nyquist limit"). For example, and 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 inclination k of the sawtooth. 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 may be associated with a predetermined periodicity of the LIDAR system 100. For example, and without 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 azimuth rotations made by the optical scanner described above. The embodiments of the present disclosure may be applied to FMCW-LIDAR (LiDAR), but are not necessarily limited to FMCW LiDAR, and may also be applied to other types of coherent LIDAR (LiDAR).

[0037] 3A is a block diagram illustrating an FMCW-LIDAR system 300 according to one embodiment. The LIDAR system 300 includes an optical scanner 301 that transmits an FMCW infrared (IR) light beam 304 and receives a return signal 313 from a reflection of the light beam 304, such as from a target 312 within a field of view (FOV) of the optical scanner 301. The LIDAR system 300 also includes an optical processor 302 that generates an electrical signal 314 (baseband signal) from the return signal 313, the electrical signal 314 having a frequency dependent on the LIDAR target distance in the time domain. In some embodiments, the optical processor 302 may include components such as the free space optics 115, the optical circuit 101, the optical driver 103, and the optical receiver 104 described in the LIDAR system 100.

[0038] The LIDAR system 300 further includes a signal processor 303. In particular, the signal processor 303 measures the energy (signal strength) of the electrical signal 314 in the frequency domain and compares the energy measurement with an estimate of the LIDAR system noise to determine the likelihood that a signal peak in the frequency domain is indicative of a detected target. In some embodiments, the signal processing device 303 may include components such as the signal conversion unit 106 , the signal conditioning unit 107 , the LIDAR controller 110 and the signal processing unit 112 in the LIDAR system 100 .

[0039] 3B is a block diagram illustrating an example of electro-optics 350 of a LIDAR system according to one embodiment. In some embodiments, electro-optics 350 includes an optical scanner 301 similar to optical scanner 102 described in FIG 1. Electro-optics 350 also includes an optical processor 302 that includes components such as free space optics 115, optical circuitry 101, optical driver 103, and optical receiver 104 described in LIDAR system 100, as described above.

[0040] The optical processing device 302 is provided with a light source 305 for generating a frequency modulated continuous wave (FMCW) light beam 304. The light beam 304 from the light source 305 is directed to an optical coupler 306, which sends a portion of the light beam 304 to a polarizing beam splitter (PBS) 307. A sample 308 (reference beam) of the light beam 304 is sent from the optical coupler 306 to a photodetector (PD) 309. The PBS 307 is configured to direct the polarized light beam 304 to the optical scanner 301. The optical scanner 301 is configured to scan the target environment with the light beam 304 at a range of azimuth and elevation angles covering a field of view (FOV) 310 of a LIDAR window 311 within the housing 320 of the electro-optical system 350. Note that only azimuth scanning is shown in FIG. 3B for simplicity of illustration.

[0041] 3B, the light beam 304 passes through the LIDAR window 311 at a predetermined azimuth angle (or range of angles) and is illuminated on the target 312. The return signal 313 from the target 312 passes through the LIDAR window 311 and is returned to the PBS 307 by the optical scanner 301.

[0042] The return signal 313, with a different polarization than the light beam 304, is guided through the PBS 307 to the photodetector (PD) 309 upon reflection from the target 312. In the photodetector (PD) 309, the return signal 313 is optically mixed with the local samples 308 of the light beam 304 to generate a time-domain distance-dependent electrical signal 314 (e.g., a beat signal). The electrical signal 314 may be generated by the frequency difference over time (i.e., ΔfR(t)) between the local samples 308 of the light beam 304 and the return signal 313.

[0043] 4 is a detailed block diagram illustrating one embodiment of a signal processor 303 for processing the electrical signal 314. As previously described, the signal processor 303 may include components such as the signal conversion unit 106, the signal conditioning unit 107, the LIDAR controller 110, and the signal processing unit 112 of the LIDAR system 100.

[0044] In some embodiments, the signal processor 303 includes an analog-to-digital converter (ADC) 401, a time domain module 402, a block sampler 403, a discrete Fourier transform (DFT) module 404, a frequency domain module 405, and a peak search module 406. Each of the constituent blocks of the signal processor 303 may be implemented, for example, in hardware, firmware, software, or a combination of hardware, firmware, and software.

[0045] As shown in Fig. 4, the electrical signal 314, which is a time-domain continuous analog signal, is sampled by the ADC 401 to generate a series of time-domain samples 315. The time-domain samples 315 are processed by the time-domain module 402 to condition the signal for further processing. For example, the time-domain module 402 may apply weighting or filtering to remove undesired signal components or to make the signal suitable for further processing. The output signal (time-domain samples) 316 of the time-domain module 402 is then sent to the block sampler 403. The block sampler 403 divides the output signal (time domain samples) 316 into groups of N samples 317 (N is an integer greater than 1) and sends them to the DFT module 404 . The DFT module 404 converts the group of N time domain samples 317 into N frequency bins or sub-bands 318 (e.g., sub-band signal spectrum 318) in the frequency domain that cover the bandwidth of the electrical signal 314. The N sub-band signal spectrum 318 is sent to the frequency domain module 405 to condition the sub-bands for further processing. For example, the frequency domain module 405 may resample and / or average the sub-band signal spectrum 318 for noise reduction. The frequency domain module 405 may also calculate signal statistics and system noise statistics. The processed sub-bands 319 (sub-band signal spectrum 319) are then sent to the peak search module 406 to search for signal peaks that represent targets within the field of view (FOV) of the LIDAR system 300.

[0046] In one embodiment, the subband signal spectrum 319 sent to the peak search module 406 is the sum of the energy of the return signal 313 from the target and all noise contributed by the LIDAR system 300 when the signal is processed. Generally, electronic systems have noise sources that limit the performance of the system by creating a noise floor, which is the combined level of all noise sources in the system. In order to be detected, a signal in an electronic system, such as the subband signal spectrum 319 generated from the electrical signal 314, must rise above the noise floor unless there are special signal processing techniques such as signal integration and noise averaging.

[0047] Noise sources in a LIDAR system such as LIDAR system 300 include thermal noise, 1 / f noise, shot noise, impulse noise, RIN (relative intensity noise associated with the laser), TIA (transimpedance amplifier) ​​noise, ADC (analog-to-digital conversion) noise, etc. System noise can be characterized, for example, by the energy vs. frequency profile across frequency bins, the first moment (mean) across frequency bins, the second moment (variance) across frequency bins, the third moment (asymmetry (skewness) across frequency bins, and / or the fourth moment (kurtosis or sharpness of the peaks) across frequency bins.

[0048] FIG. 5 is a block diagram illustrating a signal processing system 500 for automatically adjusting the detection threshold and performing peak searching, according to one embodiment. The signal processing system 500 may have the same or similar configuration as the signal processing device 303 in Fig. 3. In addition, the signal processing system 500 may include or be included as part of one or more of the components of the LIDAR system 100 in Fig. 1 (e.g., the signal conversion unit 106, the signal conditioning unit 107, the LIDAR control system 110, and the signal processing unit 112). The signal processing system 500 includes a peak search module 406 , a target prediction module 502 , and a point cloud module 504 . The peak search module 406 may receive the subband signal spectrum 319 and detect one or more peaks from the subband signal spectrum 319, as previously described (see FIGS. 6A, 6B and 7). The peak search module 406 can compare a confidence metric of the detection (e.g., a signal energy to noise ratio (SN / N)) to a detection threshold. In one example, if the confidence metric of the detection of a frequency peak exceeds the detection threshold, the peak search module 406 can identify the frequency peak as a confirmed target detection and filter out peaks that do not meet the detection threshold (e.g., false positives). In another example, for peaks that exceed the detection threshold, the peak can indicate that the peak likely corresponds to a valid target detection.

[0049] The peak search module 406 may send one or more identified peaks 515 in the subband signal spectrum 319 to the point cloud module 504. The point cloud module 504 can generate a point cloud 505 from the one or more identified peaks and other information from the frame (i.e., detected targets). Additionally, the point cloud module 504 can identify contextual information 520 (e.g., target classification, estimated target velocity, scene type, previous point cloud information) according to its proximity to the detected information, which is transmitted by the point cloud module 504 to the target prediction module 502.

[0050] The target prediction module 502 can estimate a target position 525 based on a previous detection. For example, the target prediction module 502 can estimate a current target position 525 using the context information 520. The context information 520 may include one or more previous frames related to each detection of a target, allowing the target prediction module 502 to use detections of targets in previous frames to estimate a current target position. As another example, the target prediction module 502 can estimate the target position 525 using the median position of detections in a previous frame, where the target prediction module 502 can use any number of previous frames or neighboring points of the point cloud to perform an average, median, or other calculation to estimate the target position. As yet another example, the context information 520 may include higher level information obtained from the point cloud 505 about a detected object and the predicted behavior of that object (e.g., a vehicle, a person, a stationary object, etc.).

[0051] 6A is a signal strength-frequency diagram 600 of the subband signal spectrum 319 including system noise, where the subband signal spectrum 319 is displayed as a continuous waveform (rather than discrete frequency bins or subbands) for ease of illustration. The signal strength-frequency diagram 600 may be generated and / or used by the signal processing device 303 shown in Figure 3. Additionally, the graph diagram 600 may be generated and / or used by one or more of the components of the LIDAR system 100 of Figure 1 (e.g., the signal transformation unit 106, the signal conditioning unit 107, the LIDAR control system 110, the signal processing unit 112). The frequency ranges from 0 to Δf Rmax is displayed in the range up to In some scenarios, in the absence of detailed information about the subband signal spectrum 319, the peak search module 406 selects the highest signal peak 601 as the return signal that most likely indicates the presence of a target, and does not select a lower signal, such as signal peak 602. However, if the peak search module 406 has a system noise estimate (a confidence threshold), it can compare the subband signal spectrum 319 to the system noise estimate and make a more informed selection based on additional selection criteria (i.e., a confidence index). 6A and 6B, the signal and noise values ​​are displayed as energy (signal strength) versus frequency contours, but as mentioned above, the system noise can be further characterized by any of the first through fourth moments representing the mean energy, energy variance, energy asymmetry, and kurtosis versus frequency, respectively. Also, the signal may be characterized not only in terms of energy (signal strength), but also in terms of autocorrelation statistics between frequency bins based on the baseband signal, and / or cross-correlation statistics between the signal and the system noise estimate between frequency bins.

[0052] As an example, a system noise estimate can be obtained by operating a LIDAR system, such as LIDAR system 300, in an anechoic (no-echo) calibration mode in which there are no detectable return signals, such as return signal 313. This mode of operation causes all of the normal system noise mechanisms to occur, resulting in a subband signal spectrum 319 that contains only energy from system noise sources.

[0053] 6B is a signal strength-frequency diagram 650 comparing a noise estimate 651 and the difference between the subband signal spectrum 319 and the noise estimate 651. Note that in FIG. 6B the subband signal spectrum 319 is shown as signal minus noise (SN) 661. 6B, the peak search module 406 may be configured to select the signal peak with the largest non-negative signal-to-noise ratio (SN) / N. Under this selection criterion, signal peak 652 is selected because (SN) / N 653 of signal peak 652 is greater than (SN) / N 655 of signal peak 654.

[0054] In some embodiments, the signal processor 303 can be configured to apply modifications to the subband signals (e.g., the subband signal spectrum 319) and the system noise estimate (e.g., the system noise estimate 651) to generate a confidence metric (e.g., (SN) / N) to improve the likelihood that a signal peak in the frequency domain represents a detected target and to reduce the likelihood that a signal peak from a false target in the frequency domain is interpreted as a real target. Additionally, as described above in FIG. 5 (and later in FIG. 7), the peak search module 406 may use the estimated location of the target to adjust the detection threshold of the confidence metric.

[0055] FIG. 7 shows a signal reliability versus frequency diagram 700 illustrating one example of how a threshold for peak detection may be adjusted in accordance with the present disclosure. Diagram 700 may be generated and / or used by signal processor 303 of Figure 3 or peak search module 406 of Figures 4 and 5. Additionally, diagram 700 may be generated and / or used by one or more components of LIDAR system 100 of Figure 1 (e.g., signal transformation unit 106, signal conditioning unit 107, LIDAR control system 110, and signal processing unit 112). Diagram 700 depicts three potential target detections including a strong signal target detection 705, a false positive 710, and a weak signal target detection 715. Diagram 700 also illustrates a confidence metric effectiveness threshold 720, also referred to herein as a confidence threshold, as described with respect to FIG. The confidence metric significance threshold 720 can be set to a minimum confidence threshold for identifying a signal as a detection. A strong signal target detection 705 is detected without adjustment of the significance threshold 720 because its magnitude (i.e., unadjusted) is greater than the significance threshold 720. The second potential detection is a potential false positive 710 (i.e., a peak that does not correspond to the actual target). Thus, if the significance threshold 720 is too small, the false positive 710 will be identified as an actual detection even if it does not correspond to the target. The third potential detection is a weak signal target 715. As shown in Figure 7, the weak signal target 715 does not exceed the confidence metric significance threshold 720 and is therefore not recognized as a detection.

[0056] The peak search module 406 can reduce the validity threshold 720 around the predicted location (i.e., predicted target band 725) of the weak signal target 715 to increase the probability of detection of the weak signal target (e.g., the weak signal target 715) while maintaining a low probability of false positives (e.g., the false positives 710). The predicted target band 725 can be a predefined frequency range around the predicted target frequency (i.e., the target location) or can be dynamically determined based on a confidence value associated with the estimated target location. For example, the peak search module 406 can determine a large predicted target band 725 for an estimated location with a low confidence value (e.g., when the variance associated with the estimate is high) and a small predicted target band 725 for an estimated location with a high confidence value (e.g., when the variance associated with the estimate is low). Thus, the lowered significance threshold 730 in the predicted target band 725 allows the peak search module 406 to detect targets 715 with weak signals. In addition, false positives 710 can be prevented from being detected because the significance threshold 720 is adjusted to the lowered significance threshold 730 for only a narrow frequency range. For example, the predicted target band 725 may cover frequencies associated with a physical detection distance of approximately 5 to 10 meters. It should be noted that the predicted target band 725 may thus cover any frequency range and corresponding physical distance range of the LIDAR system.

[0057] In one embodiment, a target prediction module (e.g., target prediction module 502 of FIG. 5) can estimate the location of a target previously detected with a weak signal. For example, the target prediction module 502 can estimate the location of a target previously detected with a signal having a low SNR. In another example, the target prediction module 502 may calculate estimated locations for target detections that are just above a high (i.e., unadjusted) confidence measure significance threshold 720. The peak search module 406 may accordingly lower the significance threshold for only those targets with weak signals for which estimated locations have been calculated.

[0058] FIG. 8 is a flow chart illustrating a method 800 for automatically adjusting a detection threshold based on an estimated position of a target in a LIDAR system, such as LIDAR system 100 or LIDAR system 300. The method 800 may be performed by one or more of the components of the LIDAR system 100 shown in FIG. 1 (e.g., the signal conversion unit 106, the signal conditioning unit 107, the LIDAR control system 110, the signal processing unit 112).

[0059] The method 800 begins at operation 802 where processing logic (e.g., peak search module 406) determines a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, where the frequency range includes different frequencies corresponding to the targets. The first confidence threshold may correspond to a confidence metric of the target signal. The confidence metric may be a signal-to-noise ratio (SNR), a signal minus noise-to-noise ratio ((SN) / N), or a likelihood ratio that provides a confidence value for target detection. A confidence metric may be calculated for each target signal. In determining the confidence metric, a noise estimate may be determined over the entire frequency range of the baseband signal, and a difference between the signal strength of each frequency of the baseband signal and the noise estimate over the entire frequency range may be calculated.

[0060] In operation 804, processing logic (e.g., the peak search module 406 and / or the target prediction module 502) determines a subset of frequencies within the frequency range to detect a second target, where the second target is a target that transmits a signal within the subset of frequencies that is weaker than a first confidence threshold. In some embodiments, the subset of frequencies corresponds to an estimated location of the second target. As an example, the processing logic (e.g., the target prediction module 502) may identify detections of the second target in the time domain or frequency domain and determine the estimated location of the second target based on nearby detections such as azimuth, elevation, spatial (3D point cloud), temporal (detections from previous frames), etc., to determine the estimated location of the target. As another example, the processing logic (e.g., the target prediction module 502) may identify point cloud information associated with the second target and determine the estimated location of the second target based on the point cloud information to determine the estimated location of the second target.

[0061] In operation 806, the processing logic (e.g., the peak search module 406) adjusts the first confidence threshold to a second confidence threshold in the subset of frequencies for detecting a second target within the subset of frequencies, and the processing logic (e.g., the peak search module 406) can use the second confidence threshold to filter false positive peaks that are smaller than the first detection threshold. The processing logic (e.g., peak search module 406) may then adjust the detection threshold by decreasing the detection threshold within a subset of frequencies (i.e., a target band frequency range) corresponding to the estimated location of the target. In one embodiment, the target band frequency range is a predefined frequency range near the frequency corresponding to the estimated target location. In another embodiment, the processing logic (e.g., peak search module 406) determines the width of the target band frequency range based on a confidence value associated with the estimated location of the second target.

[0062] In operation 808, processing logic (eg, peak search module 406) returns the second confidence threshold to the first confidence threshold outside the subset of frequencies for detecting the first target. In one embodiment, the processing logic (e.g., the peak search module 406) may compare the peak of the current detection of the second target to the reduced second confidence threshold. In one embodiment, if the processing logic (e.g., the peak search module 406) determines that the peak exceeds the reduced second confidence threshold, the processing logic (e.g., the peak search module 406) determines an actual location of the target based on the current detection. In one embodiment, the processing logic (e.g., the peak search module 406) may determine whether one or more previous detections of the target include a weak detection signal. The processing logic (e.g., the peak search module 406) may further determine an estimated target location and adjust a detection threshold if it determines that one or more previous detections of the target include a weak detection signal.

[0063] FIG. 9 is a block diagram of a signal processing system 900 (e.g., similar to the signal processing device 303 shown and described with respect to FIG. 4 above) in a LIDAR system, such as LIDAR system 100 or LIDAR system 300. Signal processing system 900 includes a processor 901, which may be any type of general-purpose processor or a special-purpose processor designed for use in LIDAR systems. The processor 901 is coupled to a memory 902, which may 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). The memory 902 contains instructions that, when executed by the processor 901 in the LIDAR system, cause the LIDAR system to perform the methods described herein. Specifically, the memory 902 includes instructions 904 for determining a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including different frequencies corresponding to the plurality of targets. The memory 902 also includes instructions 906 for determining a subset of frequencies within the frequency range for detecting a second target, the second target transmitting a signal on the subset of frequencies that is weaker than a first confidence threshold. The memory 902 also includes instructions 908 for adjusting the first confidence threshold to a second confidence threshold within the same subset of frequencies to detect a second target. Additionally, the memory 902 includes instructions 910 for changing the second confidence threshold back to the first confidence threshold outside the subset of frequencies for detecting the first target.

[0064] In the above description, a number of specific examples of specific systems, components, methods, etc. are shown to facilitate understanding of the embodiments of the present invention, but those skilled in the art may practice the present invention without the description of these specific examples. In addition, details of known components and methods may be omitted or shown in the form of simple block diagrams, but this is for the purpose of facilitating understanding of the present invention. Therefore, the contents disclosed are merely examples, and even if one example differs from other examples, it is considered to be included within the scope of the present invention.

[0065] When the phrase "one embodiment" or "an embodiment" is used in this specification, it 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 "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0066] Although the operations of the methods described herein are shown in a particular order, the order of operations of each method may be changed, certain operations may be performed in reverse order, at least some operations may be performed simultaneously with other operations, and instructions for different operations or auxiliary operations may be performed intermittently or alternately.

[0067] The above-described description of the 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 and examples of the invention are described herein for illustrative purposes, various equivalent modifications will occur to those skilled in the art. The word "example" or "exemplary" is used herein to mean serving as an example, illustration, 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 the 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 in any of the above cases, the condition "X contains A or B" is satisfied. Furthermore, the articles "a" and "an" as used in this specification and the appended claims are to be construed to mean "one or more" unless otherwise specified and 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 different elements and do not necessarily indicate an order according to the numerical designation.

Claims

1. A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system (hereinafter referred to as the LIDAR system). ) comprising: an optical scanner for transmitting a beam of light towards a target and receiving a return signal from the target; an optical processor coupled to the optical scanner for generating from the return signal a time domain baseband signal having a frequency dependent on the distance of the target; a signal processor connected to the optical processor, The signal processing device includes: A processor; and a memory operatively associated with the processor, the memory storing instructions that, when executed by the processor, cause the LIDAR system to: determining a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; The first confidence threshold is based on at least one of a signal to noise ratio or a likelihood ratio, which provides a confidence value for target detection; b) determining a subset of frequencies within said frequency range for detecting a second target; The second target transmits a signal within the subset of frequencies that is weaker than the first confidence threshold; c) adjusting the first confidence threshold to a second confidence threshold for detecting the second target within the subset of frequencies; d) changing the second confidence threshold back to the first confidence threshold for detecting a first target outside the subset of frequencies.

2. 2. The LIDAR system of claim 1, wherein to determine the first confidence threshold, the processor further performs the following steps: a. determining a noise estimate over said frequency range; and b) Calculate the difference between the signal strength at each frequency and the noise estimate over the frequency range.

3. 2. The system of claim 1, and wherein to determine the subset of frequencies in the frequency range for detecting the second target, the processor determines the subset of frequencies based on an estimated position of the second target.

4. The system according to claim 3, The processor further comprises: Identifying a previous detection of the second target in the time domain or frequency domain; and and determining an estimated location of the second target based on a previous detection of the second target.

5. The system according to claim 3, To determine an estimated position of the second target, the processor: Identifying point cloud information associated with the second target; and and a LIDAR system that determines an estimated location of the second target based on the point cloud information.

6. 2. The system of claim 1, the subset of frequencies includes a predefined frequency range corresponding to a position of the second target previously detected.

7. A method in a frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system, comprising the steps of: determining a first confidence threshold for detecting a first target from a plurality of targets within a frequency range, the frequency range including a plurality of different frequencies corresponding to the plurality of targets; The first confidence threshold is based on at least one of a signal to noise ratio or a likelihood ratio, which provides a confidence value for target detection. b. determining, by a processor, a subset of frequencies within said frequency range for detecting a second target; The second target transmits a signal within the subset of frequencies that is weaker than the first confidence threshold. c) adjusting, by the processor, the first confidence threshold to a second confidence threshold for detecting the second target within the subset of frequencies. d) changing the second confidence threshold back to the first confidence threshold for detecting the first target outside the subset of frequencies.

8. The method according to claim 7, comprising: The method, wherein the step of determining the first confidence threshold comprises: a. determining a noise estimate over said frequency range; and b) Calculate the difference between the signal strength at each frequency and the noise estimate over the frequency range.

9. The method of claim 7, comprising: The method of claim 1, wherein determining the subset of frequencies in the frequency range for detecting the second target comprises determining the subset of frequencies based on an estimated location of the second target.

10. The method of claim 9, further comprising: Identifying a previous detection of the second target in the time domain or the frequency domain; and determining an estimated position of the second target based on a previous detection of the second target.

11. The method of claim 9, comprising: The step of determining an estimated position of the second target includes: Identifying point cloud information associated with the second target; and determining an estimated location of the second target based on the point cloud information.

12. The method of claim 7, comprising: The subset of frequencies includes a predefined frequency range that corresponds to a position of the second target previously detected.

13. The method of claim 7, comprising: The method of claim 1, wherein adjusting the first confidence threshold comprises reducing the first confidence threshold to the second confidence threshold within a subset of the frequencies corresponding to an estimated location of the second target.

14. The method of claim 13, further comprising: determining a subset of the frequencies that corresponds to an estimated location of the second target based on a confidence value associated with the estimated location of the second target.

15. The method according to claim 7, further comprising the following steps a to c: a. determining whether one or more previous detections of the second target include a weak detection signal; b. determining an estimated location of the second target; and c) adjusting the first confidence threshold if it is determined that one or more previous detections of the second target included a weak detection signal.

16. A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system (hereinafter referred to as the LIDAR system), comprising: an optical scanner for transmitting a light beam and receiving a plurality of return signals from reflection of the light beam; an optical processor coupled to the optical scanner and configured to generate an electrical signal from the plurality of return signals; a signal processor connected to the optical processor, The signal processing device includes: A processor; and a memory operatively associated with the processor, the memory storing instructions that, when executed by the processor, cause the LIDAR system to: determining a first confidence threshold for detecting a first target for a frequency range of the electrical signal; The first confidence threshold is based on at least one of a signal to noise ratio or a likelihood ratio, which provides a confidence value for target detection; b. Estimating a location of the second target based on context information associated with the second target; and c) determining a second confidence threshold for detecting the second target, where the second confidence threshold is determined for a subset of frequencies in the frequency range, the subset of frequencies corresponding to an estimated location of the second target.

17. The LIDAR system of claim 16, The second reliability threshold is set to be less than the first reliability threshold.

18. The LIDAR system of claim 16, The subset of frequencies is determined based on an estimated location of the second target and a confidence value associated with the estimated location of the second target.

Images