Ghost Reduction Techniques in Coherent LIDAR Systems

Abstract

A light detection and ranging (LIDAR) system transmits one or more light beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within its field of view and receives one or more return signals from the target based on the one or more light beams. The LIDAR system generates baseband signals in the frequency domain of the return signals of the at least one up-chirp frequency and the at least one down-chirp frequency. The baseband signals include a first set of peaks associated with the at least one up-chirp frequency and a second set of peaks associated with the at least one down-chirp frequency, and the LIDAR system determines a location of the target using the first set of peaks and the second set of peaks.

Description

[Technical field]

[0001] This application claims priority under 35 U.S.C. §119(e) to U.S. patent application Ser. No. 17 / 495,692, filed October 6, 2021, and U.S. Provisional Patent Application Ser. No. 63 / 089,178, filed October 8, 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 reducing ghosting in coherent LIDAR systems. [Background technology]

[0003] LIDAR systems, such as frequency modulated continuous wave (FMCW) LIDAR systems, use a tunable infrared laser for frequency chirp illumination of the target and a coherent receiver to detect backscattered or reflected light from the target, which is combined with a local copy of the transmitted signal. At the receiver, the local copy is mixed with a return signal delayed by the round trip time to the target (e.g., return signal) to produce a signal with a frequency proportional to the distance to each target in the system's field of view. The frequency upsweeps and downsweeps may be used to detect the range and speed of detected targets. However, problems arise when the LIDAR system and one or more of the targets (or targets) are moving, making it difficult to accurately associate the corresponding peaks with each target, as it is necessary to determine which peaks correspond to which targets. Summary of the Invention [Means for solving the problem]

[0004] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following, aspects of the present invention, that is, aspects of a LIDAR system and method for automatically adjusting a detection threshold for target detection in order to solve the above problems, will be described.

[0005] One aspect of the present invention according to the present disclosure relates to a method, and includes the following a to c. a. Transmit one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within a field of view of a Light Detection and Ranging (LIDAR) system; b. receiving one or more return signals from the target based on the one or more light beams. wherein the one or more return signals include displaced up-chirp frequencies shifted from at least one up-chirp frequency due to at least one relative motion of the target and the LIDAR system, and displaced down-chirp frequencies shifted from at least one down-chirp frequency due to at least one relative motion of the target and the LIDAR system, the displaced up-chirp frequencies and the displaced down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; and c. determining a location of the target using the first set of peaks and the second set of peaks.

[0006] In the method according to one aspect of the present invention, the step of determining the position of the target further includes the following steps a to d. a. calculating a first set of frequency bins surrounding a first peak belonging to the first set of peaks; b. calculating a second set of frequency bins surrounding a second peak belonging to the first set of peaks based on a second set of frequency bins including negative frequency values ​​corresponding to positive frequency values ​​associated with the first set of frequency bins, where the second peak is conjugate symmetric with the first peak; c. calculating a third set of frequency bins surrounding a third peak in the second set of peaks; d. calculating a fourth set of frequency bins surrounding a fourth peak belonging to the second set of peaks based on a fourth set of frequency bins including negative frequency values ​​corresponding to positive frequency values ​​associated with the third set of frequency bins, where the fourth peak is conjugate symmetric with the third peak;

[0007] In one embodiment of the present invention, the method further includes the following steps a to f: If the first peak or the third peak is within a maximum Doppler shift for a set of minimum detectable frequencies of the LIDAR system: a. selecting a true peak value from the first peak and the third peak that corresponds to the target location based on a highest frequency having a positive frequency value; If the first peak is the true peak value: b. computing a first target distance estimate based on the first peak and the third peak, and determining a first peak shape estimate using the first target distance estimate and a phase nonlinearity of one or more light beams; c. computing a second target distance estimate based on the first peak and the fourth peak, and determining a second peak shape estimate using the second target distance estimate and a phase nonlinearity of the light beam; d. comparing the first peak shape estimate and the second peak shape estimate to the third peak and the fourth peak; e. determining that the third peak corresponds to the target location if the first peak shape estimate and the third peak show a higher correlation than the second peak shape estimate and the fourth peak; and f. If the second peak shape estimate and the fourth peak show a higher correlation than the first peak shape estimate and the third peak, determining that the fourth peak corresponds to the target location.

[0008] In one embodiment of the present invention, the method further includes the following steps a to f: If the third peak is the true peak value: a. calculating a first target distance estimate based on the first peak and the third peak, and determining a third peak shape estimate using the first target distance estimate and a phase nonlinearity of one or more light beams; b. computing a second target distance estimate based on the second peak and the third peak, and determining a fourth peak shape estimate using the second target distance estimate and a phase nonlinearity of the light beam; c. comparing the third peak shape estimate and the fourth peak shape estimate to the first peak and the second peak; d. determining that the first peak corresponds to the target location if the third peak shape estimate and the first peak exhibit a higher correlation than the fourth peak shape estimate and the second peak; and f. If the fourth peak shape estimate and the second peak show a higher correlation than the third peak shape estimate and the first peak, determining that the second peak corresponds to the target location.

[0009] In the method of one aspect of the present invention, the step of comparing the first peak shape estimate and the second peak shape estimate further comprises: The first peak shape estimate is compared to the third peak, and the second peak shape estimate is compared to the fourth peak.

[0010] In the method of one aspect of the present invention, the step of determining whether the first peak shape estimate coincides with the third peak further includes: The first peak shape estimate value and the third peak are correlated to set a correlation value, and it is determined whether the correlation value exceeds a predetermined threshold value.

[0011] In one embodiment of the present invention, the method further includes the following steps a to f: If the first or third peak is within the maximum Doppler shift relative to the Nyquist frequency: a. selecting a true peak value from the first peak and the third peak, the true peak value corresponding to the target position based on the lowest frequency having a positive frequency value; If the first peak is the true peak value: b. computing a first target distance estimate based on the first peak and the third peak, and determining a first peak shape estimate using the first target distance estimate and a phase nonlinearity of one or more light beams; c. computing a second target distance estimate based on the first peak and the fourth peak, and determining a second peak shape estimate using the second target distance estimate and a phase nonlinearity of the light beam; d. comparing the first peak shape estimate and the second peak shape estimate to the third peak and the fourth peak; e. determining that the third peak corresponds to the target location if the first peak shape estimate and the third peak show a higher correlation than the second peak shape estimate and the fourth peak; and f. if the second peak shape estimate and the fourth peak exhibit a higher correlation than the first peak shape estimate and the third peak, determine that the fourth peak corresponds to the target location, where the true peak is determined by combining a Nyquist frequency and a frequency corresponding to the fourth peak;

[0012] In one embodiment of the invention, the method further comprises: If the LIDAR system is configured with a maximum negative Doppler shift threshold: A minimum distance threshold of the LIDAR system is mapped to a maximum negative Doppler shift threshold, and positive frequency value peaks of the first and second sets of peaks are established as one or more true peaks to predict the target location.

[0013] In one aspect of the invention, the minimum distance mapping procedure further comprises: By adding an optical delay to the receive path of the LIDAR system, the minimum distance threshold is mapped to a maximum negative Doppler shift frequency.

[0014] In one aspect of the invention, the method further comprises: If the Doppler shift between the LIDAR system and the target is preset: A true peak corresponding to the target location is selected based on the first set of peaks.

[0015] In one aspect of the invention, the method further comprises: If the LIDAR system is configured with a maximum positive Doppler shift threshold: A maximum distance threshold of the LIDAR system is mapped to a maximum positive Doppler shift threshold and a chirp rate of one or more optical beams is adjusted to prevent aliasing of one or more peaks, thereby establishing positive frequency value peaks of the first and second sets of peaks as one or more true peaks to predict the target location.

[0016] In one aspect of the invention, the procedure of mapping the maximum distance threshold further comprises: The chirp rate of one or more light beams is adjusted based on the Nyquist frequency and the maximum distance threshold.

[0017] In one aspect of the invention, the method further comprises: If the first peak or the third peak is within a maximum Doppler shift relative to a minimum detection frequency of the LIDAR system: If a Doppler shift between the LIDAR system and the target is preset, and the preset Doppler shift occurs: a. selecting a first true peak position from the first peak and the third peak, the first true peak position corresponding to the target position based on a highest frequency having a positive frequency value; If the first peak is selected as the first true peak location: b. estimating a second true peak position based on the preset Doppler shift and the first peak; and c. selecting between the third peak and the fourth peak based on proximity to the second true peak location to determine a target location; If the third peak is selected as the first true peak position: d. estimating the second true peak position based on the preset Doppler shift and the third peak; and e. Selecting between the second peak and the first peak based on proximity to the second true peak location to determine a target location.

[0018] In one aspect of the invention, the pre-set Doppler shift is selected based on at least one of ego velocity, previous frame information, and nearby point information.

[0019] In one embodiment of the present invention, the method further includes the following steps a to e: If the first peak or the third peak is within a maximum Doppler shift relative to the Nyquist frequency: If a Doppler shift between the LIDAR system and the target is preset, and the preset Doppler shift occurs: a. selecting a first true peak position corresponding to the target position based on a lowest frequency having a positive frequency value among the first peak and the third peak; If the first peak is selected as the first true peak location: b. estimating a second true peak position based on the preset Doppler shift, the first peak, and a Nyquist frequency; and c. selecting between the third peak and the fourth peak based on proximity to the second true peak location to determine a target location; If the third peak is selected as the first true peak position: d. estimating a second true peak position based on the preset Doppler shift, the third peak, and the Nyquist frequency; e. selecting between the second peak and the first peak based on proximity to the second true peak location to determine the target location;

[0020] The method of one aspect of the present invention further comprises: At least one up-chirp frequency and at least one down-chirp frequency are mixed with one or more return signals to generate a baseband signal in the frequency domain, where the at least one down-chirp frequency is delayed in time proportional to the relative motion.

[0021] The method of one aspect of the present invention further comprises: The baseband signal includes the first set of peaks and the second set of peaks.

[0022] Another aspect of the present disclosure relates to a light detection and ranging (LIDAR) system comprising: 1. A light detection and ranging (LIDAR) system, comprising: An optical scanner for transmitting one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within a field of view of the LIDAR system, and receiving one or more return signals based on the one or more optical beams from the target, the optical scanner comprising: the one or more return signals include displaced up-chirp frequencies shifted from at least one up-chirp frequency due to at least one relative motion of the target and the LIDAR system, and displaced down-chirp frequencies shifted from at least one down-chirp frequency due to at least one relative motion of the target and the LIDAR system, the displaced up-chirp frequencies and the displaced down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; an optical processor coupled to the optical scanner for generating from the return signal a time domain baseband signal having a frequency corresponding to a LIDAR target distance; a signal processing device associated with the optical processing device, the signal processing device including a processor and a memory storing instructions that, when executed by the processor, operate the LIDAR system to determine the target location using the first set of peaks and the second set of peaks; Equipped with.

[0023] Another aspect of the present disclosure relates to a non-transitory computer-readable medium, comprising: 1. A non-transitory computer-readable medium comprising instructions that, when executed by a processor in a Light Detection and Ranging (LIDAR) system, cause the LIDAR system to: a. transmit one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within a field of view of the Light Detection and Ranging (LIDAR) system; b. receiving one or more return signals from the target based on the one or more light beams. wherein the one or more return signals include displaced up-chirp frequencies shifted from at least one up-chirp frequency due to at least one relative motion of the target and the LIDAR system, and displaced down-chirp frequencies shifted from at least one down-chirp frequency due to at least one relative motion of the target and the LIDAR system, the displaced up-chirp frequencies and the displaced down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; and c. determining the target location using the first set of peaks and the second set of peaks. [Brief description of the drawings]

[0024] 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.

[0025] [Figure 1] FIG. 1 is a block diagram illustrating a LIDAR system according to an embodiment of the present invention.

[0026] [Diagram 2] FIG. 2 is a time-frequency diagram illustrating an example LIDAR waveform according to an embodiment of the present invention.

[0027] [Figure 3A] FIG. 1 is a block diagram illustrating a LIDAR system according to an embodiment of the present invention.

[0028] [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.

[0029] [Figure 4] 1 is a block diagram showing a signal processing system according to an embodiment of the present invention.

[0030] [Diagram 5] FIG. 2 is a signal strength-frequency diagram showing signal peaks for a target in accordance with an embodiment of the present invention.

[0031] [Figure 6] FIG. 2 is a signal strength-frequency diagram showing signal peaks for a target in accordance with an embodiment of the present invention.

[0032] [Figure 7] FIG. 2 is a time-frequency diagram illustrating an example LIDAR waveform according to an embodiment of the present invention.

[0033] [Figure 8] FIG. 2 is a signal strength-frequency diagram illustrating frequency ranges according to an embodiment of the present invention.

[0034] [Figure 9] FIG. 2 is a block diagram illustrating an example of a signal processing apparatus for peak selection, according to an embodiment of the present invention.

[0035] [Figure 10] FIG. 2 is a signal strength-frequency diagram illustrating frequency ranges according to an embodiment of the present invention.

[0036] [Figure 11] FIG. 2 is a signal strength-frequency diagram illustrating frequency ranges according to an embodiment of the present invention.

[0037] [Figure 12]4 is a flow chart illustrating a method for peak selection according to an embodiment of the present invention.

[0038] [Figure 13] 4 is a flow chart illustrating a method for peak selection according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A LIDAR system and method for automatically reducing ghosts due to Doppler shift according to embodiments of the present invention are 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.

[0040] 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 a signal (or signals) may include a frequency up-sweep (up-chirp) and a frequency down-sweep (down-chirp), which may come from a single light source or from separate light sources (i.e., one light source for the up-sweep and one for the down-sweep). As a result, two distinct frequency peaks, one from the up-chirp and one from the down-chirp, can be associated with a target and used to determine the target's range and velocity. However, when such LIDAR systems process the signal, they can produce peak images. When these peak images are used to detect targets, they can cause targets to be detected in places where they do not exist, a phenomenon sometimes called "ghosting." The technology of the present embodiment addresses the above problem by introducing phase modulation into the up and down sweeps / chirps. This allows the LIDAR system to match the expected peak shape with the peak or peak image and distinguish between a peak (e.g., a true peak) and a peak image. The peak image may also be referred to as an "image peak."

[0041] 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 Figure 1. As shown in Figure 1, the LIDAR system 100 has an optical circuit 101 implemented on a photonics chip. The optical circuit 101 includes a combination of active and passive optical components. 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.

[0042] 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, couplers, etc. In one aspect, 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).

[0043] 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 light beam that is reflected from any objects in the environment and directs it back to the optical circuit components of the optical circuit 101. For example, the return light beam is directed to a photodetector by a polarizing beam splitter. Note that the optical scanner 102 may include quarter wave plates, lenses, anti-reflective coated optical windows, etc. in addition to mirrors and galvanometers.

[0044] 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 aspect, the processing device is one or more general-purpose processing devices, such as a microprocessor, a central processing device, 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 other instruction sets, 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.

[0045] 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 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] In some examples, 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. As an 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, the lens 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.

[0051] 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 transformed, 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 to generate a 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 .

[0052] 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.

[0053] 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 (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 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 (ΔfR max ) is 500 MHz. This limit is the system's maximum target range R max =(c / 2)(ΔfR max / 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.

[0054] 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 a FMCW (frequency modulated continuous wave) infrared (IR) light beam 304 and receives a return signal 313 from reflections of the light beam 304, such as from targets 312 within a field of view (FOV) of the optical scanner 301. The LIDAR system 300 also includes an optical processor 302 that generates a baseband signal 314 from the return signal 313, the baseband signal 314 having a frequency dependent on the LIDAR target distance in the time domain. 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. The LIDAR system 300 further includes a signal processor 303 that measures the energy of the baseband 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 indicates a detected target. The signal processor 303 may include components such as the signal transformation unit 106, the signal conditioning unit 107, the LIDAR controller 110, and the signal processing unit 112 in the LIDAR system 100.

[0055] Figure 3B is a block diagram illustrating an example of electro-optics 350 of a LIDAR system according to one embodiment. Electro-optics 350 includes an optical scanner 301 similar to optical scanner 102 described in Figure 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.

[0056] 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 polarize and direct the light beam 304 towards 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 the 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.

[0057] 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.

[0058] The return signal 313, with a different polarization than the light beam 304 due to reflection from the target 312, is guided through the PBS 307 to the photodetector (PD) 309. 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 distance-dependent baseband signal 314 in the time domain. This distance-dependent baseband signal 314 is the frequency difference between the local samples 308 of the light beam 304 and the return signal 313 versus time (i.e., Δf R (t)). The range dependent baseband signal 314 may be in the frequency domain and may be generated by mixing at least one up-chirp frequency and at least one down-chirp frequency with the return signal 313. The at least one down-chirp frequency may be delayed in time proportional to the relative motion of the target and / or the LIDAR system.

[0059] 4 is a detailed block diagram illustrating one embodiment of a signal processor 303 for processing the baseband signal 314. As previously described, the signal processor 303 may include components such as the signal transformation unit 106, the signal conditioning unit 107, the LIDAR controller 110, and the signal processing unit 112 of the LIDAR system 100.

[0060] The signal processing device 303 comprises an analog-to-digital converter (ADC) 401, a time domain signal processor 402, a block sampler 403, a discrete Fourier transform (DFT) processor 404, a frequency domain signal processor 405, and a peak search processor 406. Each of the constituent blocks of the signal processing device 303 can be implemented, for example, in hardware, firmware, software, or a combination of hardware, firmware, and software.

[0061] 4, a baseband signal 314, which is a time-domain continuous analog signal, is sampled by an ADC 401 to generate a series of time-domain samples 315. The time-domain samples 315 are processed by a time-domain signal processor 402 to condition the signal for further processing. For example, the time-domain signal processor 402 may apply weighting or filtering to remove undesired signal components or to make the signal suitable for further processing. The output signal 316 of the time-domain signal processor 402 is then sent to a block sampler 403. The block sampler 403 divides the output signal 316 of the time domain samples 315 into groups of N samples 317 (N is an integer greater than 1) and sends them to the DFT processor 404. The DFT processor 404 converts the groups of N time domain samples 317 into N frequency bins or sub-bands 318 in the frequency domain that cover the bandwidth of the baseband signal 314. The N sub-bands 318 are sent to the frequency domain signal processor 405 to condition them for further processing. For example, the frequency domain signal processor 405 may resample and / or average the sub-bands 318 for noise reduction. The frequency domain signal processor 405 may also calculate signal statistics and system noise statistics, as described below. The processed sub-bands 319 are then sent to the peak search processor 406 to search for signal peaks that represent targets within the field of view of the LIDAR system 300.

[0062] FIG. 5 is an example of a signal strength-frequency diagram 500 showing signal peaks for multiple targets in one embodiment. A LIDAR system (e.g., an FMCW LIDAR system) can generate frequency modulations of up-chirps and down-chirps (also referred to herein as upsweeps and downsweeps) to scan an environment and determine the range and velocity of targets within that environment. As one example, a single light source can generate both up-chirps and down-chirps, and as another example, a LIDAR system can have one light source that generates a signal that includes an up-chirp and another light source that generates a signal that includes a down-chirp. Using the generated beat frequencies (i.e., peak frequencies) corresponding to the return signals from the up-chirp and down-chirp, the signal processor can determine one or more of the range and / or velocity of the target. For example, the signal processing unit 112 according to one embodiment is configured to determine the range of the target by calculating the range from the LIDAR system using the multiple frequencies corresponding to the respective peaks. As previously described, the signal processing unit 112 can generate a frequency domain baseband signal by mixing at least one up-chirp frequency and at least one down-chirp frequency with one or more return signals, where the at least one down-chirp frequency may be delayed in time in proportion to at least one relative motion of the target and / or the LIDAR system. The baseband signal may include peaks 505A, 505B, 510A, and 510B, and may include additional peaks (not shown in FIG. 5).

[0063] In one embodiment, the signal processing unit 112 may be configured to use the differences in the frequencies corresponding to the peaks to determine the velocity of the target. However, as shown in FIG. 5, situations may arise where image peaks (sometimes referred to as "mirror images", "image ghosts", etc.) are present in the baseband signal. This may cause the LIDAR system to detect erroneous (or "false") targets rather than the desired "true" target or peak (sometimes referred to as "true image" or "true peak").

[0064] 5, signal strength-frequency diagram 500 includes peak 505A, peak 505B, peak 510A, and peak 510B. A frequency of zero (e.g., 0 Hertz, 0 Terahertz, etc.) is also shown in signal strength-frequency diagram 500. Peaks 505A, 505B, 510A, and 510B may be present in a baseband signal that is processed and / or analyzed by a signal processing unit (e.g., signal processing unit 112 shown in FIG. 1) of the LIDAR system, as will be described in more detail below. Peak 505B may be a mirror image of peak 505A. For example, peak 505B is flipped across frequency 0 and shares the same properties (e.g., the same curvature or shape) as peak 505A. Peak 505B may also be referred to as a peak image or image peak. Peak 505B is the conjugate mirror image of peak 505A and vice versa. Peak 510B may be a mirror image of peak 510A. For example, peak 510B is flipped across frequency 0 and shares the same properties (e.g., the same curvature or shape) as peak 510A. Peak 510B may also be referred to as a peak image or image peak. Peak 510B is the conjugate mirror image of peak 510A and vice versa. In some assumptions, peak 505A is shifted upward in frequency from the location of the target (indicated by a solid vertical line in signal strength-frequency diagram 500). Peak 505A may be referred to as an upshifted peak, a Doppler shifted peak, or a F up Peak 510A is shifted downward in frequency from the location of the target (indicated by a solid vertical line in signal strength-frequency diagram 500). Peak 510A is also referred to as a downshifted peak, a Doppler shifted peak, or F dn It is sometimes referred to as. A shift in the peak may occur due to movement of one or more of the target and / or sensor of the LIDAR system (e.g., an FMCW or similar LIDAR system) as the target moves, the device (e.g., a vehicle, a smartphone, etc.) that includes the LIDAR sensor (e.g., the optical scanner 102 and / or the optical circuit 101 shown in FIG. 1) moves, or the target and device move relative to a particular point.

[0065] Because peak 505A has been shifted upwards in frequency (upshifted), peak 505B (peak image) is located at the corresponding negative frequency. For example, if peak 505A is shifted to frequency J, peak 505B will be located at frequency -J. Also, since peak 510A has been shifted downward in frequency (downshifted), peak 510B (peak image) is located at a corresponding positive frequency. Peak 505B -F up Peak 510B is sometimes referred to as -F dn It is sometimes referred to as. In some embodiments, peak 505A (and corresponding peak 505B) may correspond to an up-chirp signal (e.g., an up-chirp signal from a particular target) and 510A (and corresponding peak 510B) may correspond to a down-chirp signal. In other embodiments, peak 505A (and corresponding peak 505B) may correspond to a down-chirp signal and 510A (and corresponding peak 510B) may correspond to an up-chirp signal (e.g., a down-chirp signal from a particular target).

[0066] In one embodiment, the LIDAR system (e.g., the signal processing unit 112 of the LIDAR system 100 shown in FIG. 1) can determine (calculate) a set of frequency bins around peak 505A and peak 505B. For example, the LIDAR system can select a first group / bin of frequencies around peak 505A (e.g., a group of frequencies that includes frequencies higher and lower than peak 505A) and a second group / bin of frequencies around peak 505B (e.g., a group of frequencies that includes frequencies higher and lower than peak 505B). The second group / bin of frequencies can include negative frequency values ​​that correspond to the positive frequency values ​​of the first bin / group of frequencies. The LIDAR system may also determine a set of frequency bins around peak 510A and peak 510B. For example, the LIDAR system may select a third group / bin of frequencies around peak 510A (e.g., a group of frequencies that includes higher and lower frequencies than peak 510A) and a fourth group / bin of frequencies around peak 510B (e.g., a group of frequencies that includes higher and lower frequencies than peak 510B). The fourth group / bin of frequencies may include positive frequency values ​​that correspond to the negative frequency values ​​of the third bin / group of frequencies.

[0067] In some embodiments, a LIDAR system (e.g., the signal processing unit 112 of the LIDAR system 100 shown in FIG. 1) can select peak 505 A. For example, when a target is in close range (e.g., within a first threshold range of the LIDAR), the peak with the highest frequency (e.g., peak 505 A) may be determined to be a true peak corresponding to the target, rather than a peak image, and selected by the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1). In this manner, the signal processing unit 112 is configured to select the peak 505A based on the type of ghost occurring (e.g., a near-distance ghost or a far-distance ghost). Thus, the LIDAR (e.g., the signal processing unit 112 shown in FIG. 1) can determine that the peak 505A should be used in determining the distance or spacing to the target. Furthermore, by the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) determining the peak 505A as a true peak (and not a peak image), the LIDAR system can also determine that the peak 505B (which has a negative frequency of the peak 505A) is a peak image.

[0068] As described above, in the LIDAR system of the embodiment, a situation may occur where a peak image (e.g., peak 505B and peak 510B) exists. For example, due to hardware or computational resources, the beat signal may undergo actual sampling and the frequency peak may be considered positive. However, when the target is closer (e.g., within the first threshold range of a LIDAR system), the negative Doppler shift can cause the beat frequency peak to become negative, e.g., due to the downshift, peak 510A has a negative frequency. In conventional systems, in contrast to the embodiments of the present disclosure, peak 510B would be selected instead of peak 510A, which may cause problems in locating the target. For example, if peak 505A and peak 510A are used, the location of the target is determined as follows: up -F dn ) / 2. Therefore, the target (the true target position) is determined towards the middle between peak 505A and peak 510A. In contrast, if peak 505A and peak 510B are used, the location of the target (the location of the ghost or ghost target) is determined as follows: up +F dn) / 2. Thus, a ghost target (represented by the dashed dotted vertical line) is detected halfway between peak 505A and peak 510B.

[0069] As shown in Figure 5, peaks 505A and 510A have a shape referred to as the peak shape or spectral shape. The spectral shape of peaks 505A and 510A can be caused (or created, generated, etc.) by phase modulation of up-chirps and down-chirps, as described in more detail below. Such phase modulation of up-chirps and down-chirps can be performed by an optical scanner (e.g., optical scanner 102 shown in Figure 1). Peaks 505B and 510B are mirror images of peaks 505A and 505B, respectively, and the shapes of peaks 505B and 510B are also mirror images of the shapes of peaks 505A and 510A. According to some embodiments, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can estimate the inherent phase mismatch of the up-chirp and down-chirp to determine the shape of the peaks 505A, 505B, 510A, and 510B, rather than adding phase modulation to the up-chirp and down-chirp.

[0070] In some embodiments, a LIDAR system (e.g., the optical scanner 102 shown in FIG. 1) can introduce phase modulations into the up-chirps and down-chirps, sometimes referred to as nonlinear phase modulations, nonlinearities, phase nonlinearities, etc. In this case, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) also processes the peak 505A as F true (e.g., first peak) can be selected. The LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1 ) may also determine a first distance estimate based on peak 510A and a second distance estimate based on peak 510B. In determining (e.g., calculating, computing, obtaining, etc.) the first distance estimate for peak 510A, the equation (F up -F dn) / 2 can be used in determining the second distance estimate for peak 510B, using the formula (F up +F dn ) / 2 can be used.

[0071] In some embodiments, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can determine a first spectral estimate 520 for peak 510A and a second spectral estimate 525 for peak 510B. For example, the first spectrum estimate 520 may be determined based on the first distance estimate and a phase modulation (eg, phase nonlinearity introduced or inherent in the chirp associated with the peak 510A). In another example, the second spectral estimate 525 can be determined based on the second distance estimate and a phase modulation (e.g., phase nonlinearity introduced or inherent in the chirp associated with the peak 510B). Note that in this disclosure, the spectral estimate may also be referred to as an estimate of shape, peak shape, etc.

[0072] In some embodiments, the LIDAR system (e.g., signal processing unit 112 shown in FIG. 1) can determine whether the first spectral estimate 520 matches the shape of peak 510A and whether the second spectral estimate 525 matches the shape of peak 510B. For example, the LIDAR system can determine that the spectral estimate 520 (e.g., peak shape) most closely matches the shape of peak 510A and that the spectral estimate 525 does not match the shape of peak 510B. This can allow the LIDAR system to select peak 510A. As mentioned above, the peak 505A has already been selected by the LIDAR system. Using this peak 505A (e.g., the first peak) and the peak 510A (e.g., the second peak), the LIDAR system can calculate the true or accurate location of the target. In this way, the LIDAR system can mitigate near-field or close-field ghosting.

[0073] In one embodiment, the LIDAR system (e.g., signal processing unit 112 shown in FIG. 1) can select either peak 505A or peak 505B as the true peak if either peak 505A or peak 505B is within the maximum Doppler shift for a minimum detection frequency set for the LIDAR system.

[0074] If peak 505A is the true peak, then signal processing unit 112 may compute (e.g., determine, calculate, obtain, etc.) a first target distance estimate based on peak 505A and peak 510A. Signal processing unit 112 may also determine a first peak shape estimate using the first target distance estimate and a phase nonlinearity of one or more light beams. Signal processing unit 112 can also compute a second target distance estimate based on peak 505A and peak 510B. Signal processing unit 112 can determine a second peak shape estimate using the second target distance estimate and the phase nonlinearity of the light beam. Signal processing unit 112 may also compare the first and second peak shape estimates to peaks 510A and 510B. Signal processing unit 112 may determine that peak 510A corresponds to the target location if the correlation between the first peak shape estimate and peak 510A is higher than the correlation between the second peak shape estimate and peak 510B. Signal processing unit 112 may determine that peak 510B corresponds to the target location if the correlation between the second peak shape estimate and peak 510B is higher than the correlation between the first peak shape estimate and peak 510A.

[0075] In one embodiment, if peak 510A is the true peak, signal processing unit 112 can calculate a first target distance estimate based on peak 505A and peak 510A, and determine a third peak shape estimate using the first target distance estimate and the phase nonlinearity of one or more light beams. The signal processing unit 112 can also calculate a second target distance estimate based on peak 505B and peak 510A, and determine a fourth peak shape estimate using the second target distance estimate and the phase nonlinearity of the light beam. Signal processing unit 112 may compare the third and fourth peak shape estimates to peak 505A and peak 505B. If the correlation between the third peak shape estimate and peak 505A is higher than the correlation between the fourth peak shape estimate and peak 505B, signal processing unit 112 may determine that peak 505A corresponds to the target location. If the correlation between the fourth peak shape estimate and peak 505B is higher than the third peak shape estimate and 505A, signal processing unit 112 may determine that peak 505B corresponds to the target location.

[0076] In one embodiment, signal processing unit 112 may perform the comparison of the first peak shape estimates by comparing the first peak shape estimate to peak 510A and comparing the second peak shape estimate to peak 510B.

[0077] In one embodiment, the signal processing unit 112 can determine whether the first peak shape estimate coincides with the peak 510A by correlating the first peak shape estimate with the peak 510A to set a correlation value and determining whether the correlation value exceeds a predetermined threshold.

[0078] In one embodiment, the LIDAR system (e.g., signal processing unit 112 shown in FIG. 1) can select either peak 505A or peak 510A as the true peak if either peak 505A or peak 510A is within the maximum Doppler shift relative to the Nyquist frequency. If peak 505A is the true peak, then signal processing unit 112 may compute (e.g., calculate, determine, obtain, etc.) a first target distance estimate based on peak 505A and peak 510A. Signal processing unit 112 may determine a first peak shape estimate using the first target distance estimate and a phase nonlinearity of one or more light beams. The signal processing unit 112 can also calculate a second target distance estimate based on peak 505A and peak 510B, and determine a second peak shape estimate using the second target distance estimate and the phase nonlinearity of the light beam. Signal processing unit 112 may then compare the first and second peak shape estimates to peak 510A and peak 510B, respectively. If the correlation between the first peak shape estimate and peak 510A is higher than the correlation between the second peak shape estimate and peak 510B, signal processing unit 112 can determine that peak 510A corresponds to the target location. If the correlation between the second peak shape estimate and peak 510B is higher than the correlation between the first peak shape estimate and peak 510A, signal processing unit 112 can determine that peak 510B corresponds to the target location. In this case, the true peak can be set as a combination of the Nyquist frequency and the frequency corresponding to peak 510B.

[0079] In one embodiment, if one or more of peaks 505A and 510A are within a maximum Doppler shift relative to a minimum detection frequency set in the LIDAR system, and a Doppler shift between the LIDAR system and the target is preset (which may produce a preset Doppler shift), the LIDAR system (e.g., signal processing unit 112) may select a first true peak location corresponding to the target location from peaks 505A and 510A based on the highest frequency with a positive value. If peak 505A is selected as the first true peak location, signal processing unit 112 can estimate a second true peak location based on a preset Doppler shift and peak 505A. In this case, signal processing unit 112 can select between peaks 510A and 510B based on their proximity to the second true peak location to determine the target location. If the peak 510A is selected as the first true peak location, the signal processing unit 112 can estimate the second true peak location based on the preset Doppler shift and the peak 510A. In this case, the signal processing unit 112 can select between the second peak (505B) and the first peak (505A) based on their proximity to the second true peak location to determine the target location. In one embodiment, the pre-set Doppler shift is selected based on at least one of ego velocity, previous frame information, and nearby point information.

[0080] In one embodiment, if one or more of peaks 505A and 510A are within the maximum Doppler shift relative to the Nyquist frequency and the Doppler shift between the LIDAR system and the target is preset (which may produce a preset Doppler shift), the LIDAR system (e.g., signal processing unit 112) may select a first true peak position corresponding to the target position from peaks 505A and 510A based on the lowest frequency with a positive value. If peak 505A is the first true peak location, signal processing unit 112 can estimate a second true peak location based on the preset Doppler shift, peak 505A, and Nyquist frequency, in which case signal processing unit 112 can select between peaks 510A and 510B based on their proximity to the second true peak location to determine the target location. If peak 510A is selected as the first true peak location, signal processing unit 112 can estimate the second true peak location based on the preset Doppler shift, peak 510A, and Nyquist frequency. In this case, signal processing unit 112 can select between peak 505A and peak 505B based on the proximity to the second true peak location to determine the target location. As previously mentioned, the pre-set Doppler shift is selected based on at least one of ego velocity, previous frame information, and nearby point information.

[0081] FIG. 6 is an example of a signal strength-frequency diagram 600 showing signal peaks for multiple targets in some embodiments. As previously discussed, a LIDAR system (e.g., LIDAR system 100 shown in FIG. 1) can generate up-chirp and down-chirp frequency modulation to scan an environment and determine the range and velocity of targets within that environment. One or more light sources can generate up-chirps and down-chirps. As previously described, using beat frequencies (i.e., peak frequencies) generated corresponding to return signals from the up-chirps and down-chirps, a signal processing system (e.g., signal processing unit 112 shown in FIG. 1) can determine one or more of the range and / or velocity of the target. As previously described, the signal processing unit 112 may generate a frequency domain baseband signal by mixing one or more return signals with at least one up-chirp frequency and at least one down-chirp frequency. The at least one down-chirp frequency may be delayed in time in proportion to the relative motion of at least one of the target and / or the LIDAR system. The baseband signal may include peaks 605A, 605B, 610A, 610B, and 630, and may include additional peaks (not shown in FIG. 6).

[0082] However, situations can arise where a peak image (e.g., image peak) exists in the baseband signal, as shown in Figure 6. Also, the peak may be shifted (e.g., Doppler shifted, upshifted, etc.) beyond the Nyquist frequency or Nyquist limit of the system LIDAR. These issues can cause the LIDAR system to detect ghosts (e.g., false targets) instead of the true targets.

[0083] 6, signal strength-frequency diagram 600 includes peak 605A, peak 605B, peak 610A, peak 610B, and peak 630. Also shown in the diagram is a frequency of 0 (e.g., 0 Hertz, 0 Terahertz, etc.). Peak 605A may be a true peak, but because peak 605A has been upshifted beyond the Nyquist frequency (e.g., Doppler shifted), peak 605A may be reflected beyond the Nyquist frequency by peak 630 (e.g., peak 630 may be a peak image beyond the Nyquist frequency of peak 605A). Peak 630 is sometimes called the Nyquist image, and peak 605A is the F true (e.g., sometimes referred to as the true peak).

[0084] Peak 605B may be a mirror image of peak 630, eg, peak 605B occurs on the opposite side of frequency 0. Peak 605B may be referred to as a peak image (eg, an image peak). Peak 610B may be a mirror image of peak 610A, eg, peak 610B occurs on the opposite side of frequency 0. Peak 610B may also be referred to as a peak image (eg, an image peak). Peak 605A is shifted upward in frequency from the location of the target (represented by the solid vertical line in signal strength versus frequency diagram 600.) Peak 605A is sometimes referred to as an upshifted peak, or a Doppler shifted peak. However, because the peak 605A has been upshifted beyond the Nyquist frequency, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) may convert the peak image 630 into F up It may be detected as Peak 610A is shifted down in frequency from the location of the target (represented by a solid vertical line in signal strength-frequency diagram 600). Peak 610A may also be referred to as a downshifted peak, a Doppler shifted peak, or a F dn It is sometimes referred to as. A shift in the peak can occur due to movement of one or more of the target and / or the LIDAR system, for example, if the target is moving, if the device containing the LIDAR system (e.g., a car, smartphone, etc.) is moving, or if both the target and the device are moving.

[0085] Peak 605B (e.g., a peak image) is located at a negative frequency corresponding to peak 630. For example, if peak 630 is at frequency J, then peak 605B will be located at frequency -J. Also, because peak 610A has been shifted downward in frequency (e.g., downshifted) to a lower frequency, peak 610B (e.g., a peak image) is located at a corresponding positive frequency. Peak 605B is at -F up , Peak 610B is F dn It is sometimes referred to as. In some embodiments, peak 605A (and corresponding peaks 605B and 630) may correspond to an up-chirp of the LIDAR system, and 610A (and corresponding peak 610B) may correspond to a down-chirp of the LIDAR system. In other embodiments, peak 605A (and corresponding peaks 605B and 630) may correspond to a down-chirp of the LIDAR system, and 610A (and corresponding peak 610B) may correspond to an up-chirp of the LIDAR system.

[0086] In some embodiments, the LIDAR system (eg, the signal processing unit 112 shown in FIG. 1) may identify (eg, calculate, compute, obtain, etc.) the peak 630. When a target is far away (e.g., beyond the second threshold range of the LIDAR), the highest frequency peak (e.g., peak 630) may not be the true peak, but rather the image may reflect the Nyquist frequency of the true peak (e.g., peak 605A) because peak 605A has been Doppler shifted or upshifted beyond the Nyquist frequency. This allows the LIDAR system to determine peak 605A based on identified peak 630. A LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can determine the peak 605A using the following formula: (2*F Nyquist -F up ), where F up represents peak 630, F Nyquist represents the Nyquist frequency.

[0087] As previously mentioned, in the LIDAR system of the embodiment, there may be situations where peak images (e.g., peaks 605B and 610B) are present. For example, due to hardware and computational resources, the beat signal may undergo actual sampling and the frequency peaks may be considered positive. However, if the target is closer (e.g., within the first threshold range of the LIDAR), a negative Doppler shift may cause the beat frequency peaks to decrease (shift downward). For example, peak 610A is downshifted and decreased in frequency. In such a case, the LIDAR system (eg, the signal processing unit 112 shown in FIG. 1) may use peak 610B instead of peak 610A when determining the location of the target (eg, the target position). For example, if peak 605A and peak 610A are used, the target location is determined as follows: ((2*F Nyquist -F up )-F dn) / 2. Thus, the target (e.g., the true target position) is determined to be midway between peak 605A and peak 610A. However, if peak 630 and peak 610B are used, an incorrect target location (e.g., the location of a ghost or ghost target) may be determined as follows: up +F dn ) / 2. Therefore, the first ghost target (e.g., Ghost 1) is detected halfway between peak 630 and peak 610B. Furthermore, if peak 630 and peak 610A are used, an incorrect target location may be determined as follows: (F up -F dn ) / 2. Thus, a second ghost target (e.g., ghost 2) may be detected halfway between peak 630 and peak 610A.

[0088] As shown in Figure 6, peaks 605A and 610A have a shape that may be referred to as a peak shape or spectral shape. The spectral shape of peaks 605A and 610A may be due to (or created, generated, etc.) phase modulation of up-chirps and down-chirps, as described in more detail below. Peaks 605B and 610B are mirror images of peaks 605A and 610A, respectively, and the shapes of peaks 605B and 610B are also mirror images of the shapes of peaks 605A and 605B. According to some embodiments, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can estimate the inherent phase mismatch of the up-chirp and down-chirp to determine the shape of the peaks 605A, 605B, 610A, and 610B, rather than adding phase modulation to the up-chirp and down-chirp.

[0089] In some embodiments, the LIDAR system may introduce phase modulation into the up-chirp and down-chirp. The modulation of the up-chirp and down-chirp may be performed by the optical scanner of the LIDAR system (e.g., the optical scanner 102 shown in FIG. 1). These phase modulations may also be referred to as nonlinear phase modulations, nonlinearities, phase nonlinearities, etc. As previously mentioned, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) may detect peak 605A (e.g., the first peak or F true ) may also be determined (e.g., calculated, generated, etc.). A LIDAR system (eg, signal processing unit 112 shown in FIG. 1) may also use peak 610A to determine a first distance estimate and peak 610B to determine a second distance estimate. To determine (e.g., calculate, compute, obtain, etc.) a first distance estimate for peak 610A, use the formula ((2*F Nyquist -F up )-F dn ) / 2 can be used. To determine the second distance estimate for peak 610B, the formula ((2*F Nyquist -F up ) +F dn ) / 2 can be used.

[0090] In some embodiments, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can determine a first spectral estimate 620 for peak 610A and a second spectral estimate 625 for peak 610B. For example, a first spectrum estimate 620 may be determined based on the first distance estimate and a phase modulation (eg, a phase nonlinearity introduced or inherent in a chirp associated with peak 610A). In another example, the second spectral estimate 625 is determined based on the second distance estimate and a phase modulation (e.g., a phase nonlinearity introduced or inherent in a chirp associated with the peak 610B). The spectral estimate may also be referred to as a shape value, a peak shape value, etc.

[0091] In some embodiments, the LIDAR system (e.g., the signal processing device 303 shown in FIG. 3A) can determine whether the first spectral estimate 620 matches the shape of peak 610A and whether the second spectral estimate 625 matches the shape of peak 610B. For example, the LIDAR system may determine that the spectral estimate 620 (e.g., peak shape) matches the shape of peak 610A, while the spectral estimate 625 does not match the shape of peak 610B, which may allow the LIDAR system to select peak 610A. As explained above, peak 605A has previously been determined (e.g., calculated, generated, etc.) by the LIDAR system. Thus, using peak 605A and peak 610A, the LIDAR system can calculate the true or correct location of the target. In this manner, the LIDAR system can reduce long-distance ghosting.

[0092] FIG. 7 is a time-frequency diagram 700 of an FMCW scan signal 701 and an FMCW scan signal 702 that can be used by a LIDAR system, such as LIDAR system 100, in some embodiments to scan a target environment. FMCW scanning signal 701 increases in frequency over time and is therefore sometimes referred to as an up-chirp, up-sweep, etc. FMCW scanning signal 702 decreases in frequency over time and is therefore sometimes referred to as a down-chirp, down-sweep, etc.

[0093] As previously discussed, phase modulation (e.g., nonlinear phase modulation, nonlinearity, phase nonlinearity) may be added or introduced into the FMCW scanning signal. For example, FMCW scanning signal 711 includes phase modulation, and FMCW scanning signal 712 also includes phase modulation. The phase modulation (e.g., modulating waveform) is intended to allow the FMCW-LIDAR (e.g., LIDAR system) to detect the difference between the peak (e.g., true peak) and the peak image, but does not distort (e.g., blur) the peak image to the extent that it becomes difficult to detect. As mentioned before, the spectral shape of the peak (e.g., shape, peak shape) may also depend on the distance to the target. Because the received signal also has the same phase modulation, the spectral shape may also depend on the delay between the transmitted and received signals. Doppler shift can affect the frequency of the peak but not the spectral shape of the peak. Therefore, for targets at different distances, the peaks can have different spectral shapes with the same frequency. This allows the FMCW-LIDAR to determine which peak is the actual peak (true peak) or the peak image from multiple peaks.

[0094] 8 is a signal strength-frequency diagram 800 illustrating a frequency range according to an embodiment. In the signal strength-frequency diagram 800, a frequency of 0 (e.g., 0 Hertz, 0 Terahertz, etc.) is shown. Signal strength-frequency diagram 800 shows frequency D MAX,DN Also shown. D MAX,DN is set to the maximum or threshold negative Doppler shift that the LIDAR system can take into account when detecting an object (i.e., the Doppler shift that occurs when an object moves away from the LIDAR system). Signal strength-frequency diagram 800 is also D MAX,up Also shown is D MAX,up is set to the maximum or threshold positive Doppler shift that the LIDAR system can take into account when detecting an object (i.e., the Doppler shift that occurs when an object moves toward the LIDAR system). The signal strength-frequency diagram 800 shows the Nyquist frequency F NYIQUIST Also shown in the signal strength-frequency diagram 800 is the frequency F NYIQUIST -D MAX,up is also shown.

[0095] In Figure 8, 0 and D MAX,DN The frequency range between is the first frequency range where near / close ghosting can occur. F NYIQUIST -D MAX,up and F NYIQUIST The frequency range between is the second frequency range in which distant ghosts can occur. D MAX,DN and F NYIQUIST -D MAX,up The frequency range between is a third frequency range where ghosting is less likely to occur.

[0096] The FMCW-LIDAR system according to this embodiment can analyze the detected peaks to determine whether a near / close ghost or a far ghost is occurring. In some embodiments, the positive peak of the first chirp / sweep is D MAX,DN and the positive peak of the second chirp / sweep is smaller than 2*D MAX,DN If less than , near field ghost reduction is applied. In another embodiment, the positive peak of the first chirp / sweep is F NYIQUIST -D MAX,up and the positive peak of the second chirp / sweep is (F NYIQUIST -(2*D MAX,up )), then long-distance ghosting reduction is applied. Furthermore, both positive peaks are in the range (D MAX,DN and F NYIQUIST -D MAX,up ), ghosting reduction will not be applied.

[0097] In some embodiments, the FMCW-LIDAR system can use energy detection instead of peak detection to determine whether to use near-field or far-field ghost reduction. As an example, peak detection may use more computational resources (eg, processing resources, amount of processing, processing power) and / or memory, and peak detection may take more time to perform. In this embodiment, detecting the total amount of energy in a frequency range (e.g., energy detection) allows the FMCW-LIDAR to more quickly and efficiently determine what type of ghost reduction to use than detecting peaks.

[0098] FIG. 9 is a block diagram of an example peak selector 900 for selecting (eg, determining, selecting, calculating, etc.) a peak, according to one embodiment. The peak selector 900 may be configured as part of a signal processor of a LIDAR system. For example, the peak selector 900 may be part of the signal processor 303 of the LIDAR system 300, as shown in Figures 3A and 4. As another example, the peak selector 900 may be part of the signal processor 112 shown in Figure 1. In particular, the peak selector 900 may be included in the peak search processor 406 of the signal processor 303, as shown in Figure 4. The peak selector includes a distance module 905 , distance estimation modules 910 A and 910 B, spectral estimation modules 915 A and 915 B, spectral matching modules 920 A and 920 B, and a comparison module 925 .

[0099] The distance module 905 may be configured in hardware, software, firmware, or a combination thereof. In one embodiment, the distance module 905 can determine whether near or far ghosting is occurring (e.g., whether near or far ghost reduction should be applied) based on whether the peaks detected by the LIDAR system are in different frequency ranges, as described in FIG. 8. The distance module 905 may perform peak detection or energy detection as previously described.

[0100] Distance estimation modules 910A and 910B may be implemented in hardware, software, firmware, or a combination thereof. In one embodiment, distance estimation modules 910A and 910B may determine (eg, calculate, generate, etc.) different distance estimates for different peaks. As an example, and referring to FIG. 5, distance estimation module 910A may determine a distance estimate for peak 510A, and distance estimation module 910B may determine a distance estimate for peak 510B.

[0101] The spectral estimation modules 915A and 915B may be implemented in hardware, software, firmware, or a combination thereof. In one embodiment, the spectral estimation modules 915A and 915B may determine (eg, generate, calculate, etc.) different spectral estimates (eg, different peak shapes) for different peaks. 5 as an example, the spectral estimation module 915A can determine a first spectral estimate 520 of the peak 510A based on the first distance estimate and the phase modulation of the chirp / sweep associated with the peak 510A. The spectral estimation module 915B can determine a second spectral estimate 525 of the peak 510B based on the first distance estimate and the phase modulation of the chirp / sweep associated with the peak 510B.

[0102] Spectral matching modules 920A and 920B may be implemented in hardware, software, firmware, or a combination thereof. In one embodiment, the spectral matching modules 920A and 920B can determine whether the spectral estimates (generated by the spectral estimation modules 915A and 915B) match the shape of the peaks detected by the LIDAR system. In some embodiments, the spectral matching modules 920A and 920B can use a correlation filter to select the peak that better matches one of the spectral estimates, and the peak with the higher correlation result is selected as the true peak. In other embodiments, the spectral matching modules 920A and 920B may use a phase correction to select a peak that better matches one of the spectral estimates. The spectral estimates can be used to make the peak taller and narrower by removing the phase modulation. The modified taller and narrower peak can then be selected.

[0103] In one embodiment, the spectrum matching modules 920A and 920B can each output a match level to the comparison module 925. The match level is an index, parameter, number, or other value that indicates how close the shape of the peak is to the spectrum estimate. The comparison module 925 can select the peak with the highest match level.

[0104] 10 is a signal strength-frequency diagram 1000 illustrating a frequency range according to an embodiment of the present disclosure. The signal strength-frequency diagram 1000 shows a frequency of 0 (e.g., 0 Hertz, 0 Megahertz, 0 Gigahertz, 0 Terahertz, etc.). Signal strength-frequency diagram 1000 shows frequency D MAX,DN Also shown. D MAX,DN is set to the maximum or threshold negative Doppler shift that a LIDAR system (e.g., LIDAR system 100 shown in FIG. 1) can consider when detecting an object. Signal strength-frequency diagram 1000, D MAX,UP Also shown is D MAX,UP is set to the maximum or threshold positive Doppler shift that the LIDAR system can take into account when detecting an object. The signal strength-frequency diagram 1000 shows the Nyquist frequency F NYIQUIST Additionally, the signal strength-frequency diagram 1000 also shows the frequency F NYIQUIST -D MAX,up is also shown.

[0105] As mentioned above, 0 to D MAX,DN The frequency range between is the first frequency range where near / close ghosting can occur. F NYIQUIST -D MAX,up and F NYIQUIST The frequency range between is the second frequency range where distant ghosts can occur. D MAX,DN and F NYIQUIST -D MAX,up The frequency range between is a third frequency range where ghosting is less likely to occur.

[0106] In one embodiment, the receive path of the LIDAR system may include a delay. 3A as an example, the signal processor 303 of a LIDAR system can activate components within the optical processor to delay the baseband signal 314 provided to the signal processor 303. The delay in the receive path allows the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) to detect a target at zero (0) meters distance at frequency D MAX,DN can be mapped to For example, the frequency (e.g., F range ,0) is the frequency D MAX,DN This ensures that the frequency peaks of all targets are always positive, even when the maximum negative Doppler shift occurs. By ensuring that the frequency peaks are always positive (even at the maximum negative Doppler shift), LIDAR systems can reduce and / or eliminate the possibility of near-field ghosting.

[0107] In one embodiment, a target at a distance of zero (0) meters is detected by a frequency D MAX,DN By mapping to the LIDAR system, the resolution (e.g. range resolution) can be changed. In such an embodiment, the total number of frequencies is reduced (from 0 to frequency D MAX,DN , which can reduce the resolution of the LIDAR system), may increase the amount of distance each frequency represents (because frequencies up to 1000 MHz are not used). In other embodiments, the LIDAR system may have a threshold resolution (e.g., a maximum distance represented by each frequency). If the resulting resolution of the LIDAR system is less than the threshold resolution, the LIDAR system may not use delays in the receive path (detecting a target at a distance of 0 meters as frequency D). MAX,DN (to map to). In yet another embodiment, the LIDAR system detects targets at the maximum range of the LIDAR system at a frequency F NYIQUIST -D MAX,up and maps targets at 0 meters to frequency D MAX,DN can be mapped to

[0108] In one embodiment, the LIDAR system (e.g., the signal processor 303 of the LIDAR system) can map the minimum distance threshold of the LIDAR system to a maximum negative Doppler shift threshold if the maximum negative Doppler shift threshold is set (e.g., configured) for the LIDAR system. In this case, positive frequency value peaks between the first and second sets of peaks (e.g., peaks 505A and 505B are the first set of peaks, and peaks 510A and 510B are the second set of peaks: see FIG. 5) may be established, selected, identified, used, etc. as one or more true peaks for predicting the location of the target. In another embodiment, the signal processor 303 can map the minimum distance threshold of the LIDAR system to a maximum negative Doppler shift threshold by adding an optical delay to the receive path of the LIDAR system to map the minimum distance threshold to a maximum negative Doppler shift frequency.

[0109] FIG. 11 is a signal strength-frequency diagram 1100 illustrating frequency ranges according to an embodiment of the present disclosure. Signal strength versus frequency diagram 1100 shows zero frequency (eg, 0 Hertz, 0 Megahertz, 0 Gigahertz, 0 Terahertz, etc.). Signal strength-frequency diagram 1100 shows frequency D MAX,DN Also shown. D MAX,DN is set to the maximum or threshold negative Doppler shift that a LIDAR system (e.g., LIDAR system 100 shown in FIG. 1) can consider when detecting an object. Signal strength-frequency diagram 1100 is D MAX,UP Also shown is D MAX,UP is set to the maximum or threshold positive Doppler shift that the LIDAR system can take into account when detecting an object. The signal strength-frequency diagram 1100 shows the Nyquist frequency F NYIQUIST Additionally, the signal strength vs. frequency diagram 1100 also shows the frequency F NYIQUIST -D MAX,up is also shown.

[0110] As mentioned above, 0 to D MAX,DN The frequency range between is the first frequency range where near / close ghosting can occur. F NYIQUIST -D MAX,up and F NYIQUIST The frequency range between is the second frequency range where distant ghosts can occur. MAX,DN and F NYIQUIST -D MAX,up The frequency range between is a third frequency range where ghosting is less likely to occur.

[0111] In one embodiment, the rate at which a chirp (eg, an up-chirp or a down-chirp) increases / decreases in frequency (the chirp rate) can be adjusted, changed, tuned, etc. As an example, and referring to FIG. 1, the signal processing unit 112 can instruct the optical scanner 102 to change the rate at which the up-chirp and down-chirp frequencies are changed. By changing (e.g., modifying, adjusting, etc.) the rate at which the frequency of the chirp (e.g., the chirp rate) increases / decreases, the LIDAR system (e.g., the signal processing unit 112 shown in FIG. 1) can detect targets at the maximum range of the LIDAR system at a frequency F NYIQUIST -D MAX,up can be mapped to For example, the frequency of an object located at the maximum distance of a LIDAR system (e.g., F range,max ) is the frequency F NYIQUIST -D MAX,up This maps all targets' frequency peaks to F NYQUIST The frequency peak is guaranteed not to exceed F NYQUIST By ensuring that no more than 100% of the signal is received, a LIDAR system can reduce and / or eliminate the possibility of long-distance ghosting.

[0112] In one embodiment, the LIDAR system detects targets at the maximum distance using a frequency F NYIQUIST -D MAX,up By mapping to the LIDAR system, the resolution (e.g. range resolution) can be changed. In such an embodiment, the total number of frequencies is reduced (F NYIQUIST -D MAX,up and F NYIQUIST , since the frequencies between are unused), may increase the amount of distance each frequency represents (which may reduce the resolution of the LIDAR system). In other embodiments, the LIDAR system may have a threshold resolution (e.g., a maximum distance represented by each frequency). If the resulting resolution of the LIDAR system is below the threshold resolution, the rate at which the chirp frequency is changed can be adjusted so that the LIDAR system is below the threshold resolution (targets at the maximum distance of the LIDAR system can be detected at a frequency of F). NYIQUIST -D MAX,up (to map to). In yet another embodiment, the LIDAR system detects targets at the maximum range of the LIDAR system at a frequency FNYIQUIST -D MAX,up , and the target at a distance of 0 meters is mapped to frequency D MAX,DN can be mapped to

[0113] In one embodiment, the LIDAR system (e.g., the signal processing unit 112) can adjust the chirp rate of one or more light beams to map a maximum distance threshold of the LIDAR system to a maximum positive Doppler shift threshold when the maximum positive Doppler shift threshold is set (e.g., configured) in the LIDAR system. By adjusting the chirp of the light beam in this manner, the positive frequency value peaks of the first and second sets of peaks can be established as one or more true peaks for predicting the location of the target, thereby preventing (or helping to prevent) aliasing of the one or more peaks. In other embodiments, the signal processing unit 112 may map the maximum distance threshold to the Nyquist frequency by adjusting the chirp rate of one or more light beams.

[0114] FIG. 12 is a flow chart illustrating a method 1200 for selecting a peak in a LIDAR system, such as LIDAR system 100 or LIDAR system 300, according to an embodiment of the present disclosure. The method 1200 may be performed by processing logic comprised of hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, processing device, central processing unit (CPU), system on chip (SoC), etc.), software (e.g., instructions executed / performed by a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, the method 1200 may be performed by a signal processor of a LIDAR system (e.g., the signal processor 303 of the LIDAR system 300 shown in Figures 3A and 4).

[0115] The method 1200 begins at operation 1201 with processing logic determining whether to adjust one or more ghost ranges. For example, see Figures 10 and 11. The processing logic may determine whether near and / or far ghosts are reduced or eliminated based on a threshold resolution. If the one or more ghost ranges are adjusted, the processing logic may add receive delay and / or adjust the rate of change of the up-chirp and / or down-chirp frequency at operation 1202. For example, if the resolution of the LIDAR system is less than the threshold resolution, the processing logic adds receive delay and / or adjusts the rate of change of the up-chirp and / or down-chirp frequency. The processing logic, in operation 1203, transmits one or more optical beams including up-chirp and down-chirp frequency modulation toward a target within a field of view of a Light Detection and Ranging (LIDAR) system, receives one or more return signals, generates a baseband signal, identifies a peak in the baseband signal, and determines a distance to the target based on the peak. For example, the processing logic may select the two highest peaks in the baseband signal and use these two highest peaks to determine the range to the target.

[0116] If the ghost range does not need to be adjusted, method 1200 proceeds to operation 1205, where the processing logic transmits one or more optical beams including up-chirp frequency modulation and down-chirp frequency modulation toward a target within a field of view of a Light Detection and Ranging (LIDAR) system. Optionally, the processing logic may introduce phase modulation into the one or more optical beams. Then, in operation 1210, the processing logic receives one or more return signals of the up-chirp and down-chirp reflected from the target.

[0117] In operation 1215, the processing logic generates a baseband signal in the frequency domain of one or more return signals of the up chirp and down chirp, the baseband signal including a set of peaks associated with targets detected by the up chirp and down chirp, the set of peaks including a first peak, a second peak, a first peak image, and a second peak image. For example, the set of peaks may include the peaks and / or peak images shown in FIGS.

[0118] In operation 1220, the processing logic selects one or more of the first and second peaks based on the peak shape. For example, the processing logic may determine the first frequency range, the second frequency range, and the third frequency range based on a threshold (e.g., maximum) of a Doppler shift of the LIDAR system. The processing logic may determine a frequency range for each peak (eg, whether it is a first frequency range, a second frequency range, or a third frequency range). Based on whether the peak is located in the first frequency range, the second frequency range, or the third frequency range, the processing logic can determine whether the highest frequency peak should be selected as the first peak, or whether a third peak should be determined (e.g., calculated, determined, etc.) and used as the first peak. For example, if the near / close ghosting occurs because of a peak in a first frequency range, the processing logic can select the highest frequency peak, and if the far ghosting occurs because of a peak in a second frequency range, a new peak can be calculated based on the first peak (e.g., a Nyquist image) as described above. The processing logic can also select a second peak based on the peak shape, for example, the processing logic can determine one or more spectral shapes (described above) and compare them to the shapes of the peaks in the peak set to select a second peak from the peak set.

[0119] In operation 1225, the processing logic determines a distance to the target based on the first peak and the second peak, for example, by summing the frequency of the first peak and the frequency of the second peak and dividing by two.

[0120] FIG. 13 is a flow chart illustrating a method 1300 in a LIDAR system (e.g., LIDAR system 100 or LIDAR system 300) for selecting a peak in accordance with an embodiment of the present disclosure. The method 1300 may be performed by processing logic comprised of hardware (e.g., a circuit, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system on a chip (SoC), etc.), software (e.g., instructions executed / performed on a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, the method 1300 is performed by a signal processor of a LIDAR system (e.g., the signal processor 303 of the LIDAR system 300 shown in FIGS. 3A and 4).

[0121] In one embodiment, the method 1300 may be performed when the total Doppler shift (eg, the relative velocity between the LIDAR system or a sensor of the LIDAR system and the target) is known. The processing logic may determine or estimate the total Doppler shift (D) using various techniques. For example, the processing logic may determine the total Doppler shift by estimating or determining the ego-velocity. If the target is stationary or not moving, then ego velocity may be used. As another example, the processing logic may use information / data from a previous frame. For each point in a point cloud (e.g., a 3D point cloud), the processing logic may assume that the Doppler shift is the same as the same point in the previous frame. As yet another example, the processing logic can use information / data from neighboring points: if the Doppler shifts of the previous N points are in the vicinity of the current point and have a small variance, the Doppler shift of the current point can be equal to the average or median of the neighboring points.

[0122] If the total Doppler shift is known, the processing logic may select the first true peak (eg, the most likely true peak) in operation 1305 . When near-field ghosting occurs, the first true peak F true1 is F true1 =F up (e.g. an upward shifted peak). When long-distance ghosting occurs, the first true peak F true1 is F true1 =F dn (e.g. a downward shifted peak).

[0123] In operation 1310, the processing logic can estimate the location of the other (second) true peak using the known Doppler shift. If a near-field ghost occurs, the other (second) true peak F dn The estimated location of F dn =F up Sometimes it is determined as -2*D. If a distant ghost occurs, the other (second) true peak F up The estimated location of F up =2*F Nyquist -(F dn+2*D).

[0124] In operation 1315, the processing logic may select a peak that is within a threshold, tolerance, etc. of the estimated position (eg, estimated peak position) as a second true peak. For example, if a close-distance ghost occurs, the processing logic is F dn Peaks that are within a threshold of can be selected. As another example, if a long-distance ghost is occurring, the processing logic is F up Peaks that are within a threshold of can be selected.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 method comprising the following steps a to c: Transmit one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within a field of view of a Frequency Modulated Continuous Wave (FMCW) Light Detection and Ranging (LIDAR) system (hereinafter referred to as the LIDAR system), the optical beams being phase modulated such that the at least one up-chirp and the at least one down-chirp have a phase nonlinearity; b) receiving one or more return signals from said target based on said one or more light beams; wherein the one or more return signals include a displacement up-chirp frequency shifted from at least one up-chirp frequency due to at least one relative motion of the target and the LIDAR system, and a displacement down-chirp frequency shifted from at least one down-chirp frequency due to at least one relative motion of the target and the LIDAR system, the displacement up-chirp frequencies and the displacement down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; and c) using the first set of peaks and the second set of peaks to determine the location of the target while detecting a peak image based at least in part on the shape of the first set of peaks and the second set of peaks caused by phase nonlinearities in the one or more light beams.

2. 2. The method of claim 1, wherein the step of determining the position of the target further comprises: a. Calculate a first set of frequency bins surrounding a first peak in the first set of peaks; b) calculating a second set of frequency bins surrounding a second peak belonging to the first set of peaks based on a second set of frequency bins including negative frequency values ​​corresponding to positive frequency values ​​associated with the first set of frequency bins, where the second peak is conjugate symmetric with the first peak; c. Calculate a third set of frequency bins surrounding a third peak in the second set of peaks; d) calculating a fourth set of frequency bins surrounding a fourth peak belonging to the second set of peaks based on a fourth set of frequency bins including negative frequency values ​​corresponding to positive frequency values ​​associated with the third set of frequency bins, where the fourth peak is conjugate symmetric with the third peak;

3. The method according to claim 2, further comprising the following steps a to f: If the first peak or the third peak is within a maximum Doppler shift for a minimum detection frequency set of the LIDAR system: a. Select a true peak value from the first peak and the third peak that corresponds to the target location based on a highest frequency having a positive frequency value; When the first peak is the true peak value: b. computing a first target distance estimate based on the first peak and the third peak, and determining a first peak shape estimate using the first target distance estimate and a phase nonlinearity of the one or more light beams; c) computing a second target distance estimate based on the first peak and the fourth peak, and determining a second peak shape estimate using the second target distance estimate and a phase nonlinearity of the one or more light beams; d. comparing the first peak shape estimate and the second peak shape estimate to the third peak and the fourth peak; e) determining that the third peak corresponds to the target location if the first peak shape estimate and the third peak exhibit a higher correlation than the second peak shape estimate and the fourth peak; f) if the second peak shape estimate and the fourth peak exhibit a higher correlation than the first peak shape estimate and the third peak, determine that the fourth peak corresponds to the target location.

4. The method according to claim 3, further comprising the following steps a to f: When the third peak is the true peak value: a) calculating a first target distance estimate based on the first peak and the third peak, and determining a third peak shape estimate using the first target distance estimate and a phase nonlinearity of the one or more light beams; b. computing a second target distance estimate based on the second peak and the third peak, and determining a fourth peak shape estimate using the second target distance estimate and a phase nonlinearity of the one or more light beams; c. comparing the third peak shape estimate and the fourth peak shape estimate to the first peak and the second peak; d. determining that the first peak corresponds to the target location if the third peak shape estimate and the first peak exhibit a higher correlation than the fourth peak shape estimate and the second peak; and f) if the fourth peak shape estimate and the second peak exhibit a higher correlation than the third peak shape estimate and the first peak, determine that the second peak corresponds to the target location.

5. 4. The method of claim 3, wherein the step of comparing the first and second peak shape estimates further comprises: The first peak shape estimate is compared to the third peak, and the second peak shape estimate is compared to the fourth peak.

6. 4. The method of claim 3, if the first peak shape estimate coincides with the third peak, further comprising: The first peak shape estimate value and the third peak are correlated to set a correlation value, and it is determined whether the correlation value exceeds a predetermined threshold value.

7. The method according to claim 2, further comprising the following steps a to f: If the first peak or the third peak is within the maximum Doppler shift relative to the Nyquist frequency: a. selecting a true peak value from the first peak and the third peak that corresponds to the target location based on the lowest frequency that has a positive frequency value; If the first peak is the true peak value: b. computing a first target distance estimate based on the first peak and the third peak, and determining a first peak shape estimate using the first target distance estimate and a phase nonlinearity of the one or more light beams; c) computing a second target distance estimate based on the first peak and the fourth peak, and determining a second peak shape estimate using the second target distance estimate and a phase nonlinearity of the one or more light beams; d. comparing the first peak shape estimate and the second peak shape estimate to the third peak and the fourth peak; e) determining that the third peak corresponds to the target location if the first peak shape estimate and the third peak exhibit a higher correlation than the second peak shape estimate and the fourth peak; f) if the second peak shape estimate and the fourth peak show a higher correlation than the first peak shape estimate and the third peak, determine that the fourth peak corresponds to the target location, where the true peak is set at a combination of the Nyquist frequency and a frequency corresponding to the fourth peak.

8. 10. The method of claim 1, further comprising: If the LIDAR system is configured with a maximum negative Doppler shift threshold: A minimum distance threshold of the LIDAR system is mapped to a maximum negative Doppler shift threshold, and positive frequency value peaks of the first and second sets of peaks are established as one or more true peaks to predict the target location.

9. 9. The method of claim 8, wherein the minimum distance mapping step further comprises: By adding optical delay to the receive path of the LIDAR system, the minimum distance threshold is mapped to a maximum negative Doppler shift frequency.

10. 9. The method of claim 8, further comprising: If the Doppler shift between the LIDAR system and the target is preset: A true peak corresponding to the target location is selected based on the first set of peaks.

11. 9. The method of claim 8, further comprising: If the LIDAR system is configured with a maximum positive Doppler shift threshold: A maximum distance threshold of the LIDAR system is mapped to a maximum positive Doppler shift threshold and a chirp rate of one or more light beams is adjusted to prevent aliasing of one or more peaks, thereby establishing positive frequency value peaks of the first and second sets of peaks as one or more true peaks to predict the target location.

12. 11. The method of claim 10, wherein the procedure of mapping the maximum distance threshold further comprises: The chirp rate of one or more light beams is adjusted based on the Nyquist frequency and the maximum distance threshold.

13. 3. The method of claim 2, further comprising: If the first peak or the third peak is within a maximum Doppler shift relative to a minimum detection frequency of the LIDAR system: If a Doppler shift between the LIDAR system and the target is preset, and the preset Doppler shift occurs: a. Select a first true peak location from the first peak and the third peak, the first true peak location corresponding to the target location based on a highest frequency having a positive frequency value; If the first peak is selected as the first true peak location: b) estimating a second true peak position based on the predetermined Doppler shift and the first peak; and c. Selecting between the third peak and the fourth peak based on their proximity to the second true peak location to determine a target location; If the third peak is selected as the first true peak location: d) estimating the second true peak position based on the preset Doppler shift and the third peak; and e) Selecting between the second peak and the first peak based on proximity to the second true peak location to determine a target location.

14. 14. The method of claim 13, A method, wherein the pre-set Doppler shift is selected based on at least one of ego-velocity, previous frame information, and neighboring point information.

15. The method according to claim 2, further comprising the following steps a to e: When the first peak or the third peak is within a maximum Doppler shift relative to the Nyquist frequency: If a Doppler shift between the LIDAR system and the target is preset, and the preset Doppler shift occurs: a. selecting a first true peak position corresponding to the target position based on a lowest frequency having a positive frequency value among the first peak and the third peak; If the first peak is selected as the first true peak location: b) estimating a second true peak position based on the predetermined Doppler shift, the first peak, and a Nyquist frequency; c. selecting between the third peak and the fourth peak based on proximity to the second true peak location to determine a target location; If the third peak is selected as the first true peak location: d. Estimating a second true peak location based on the preset Doppler shift, the third peak, and the Nyquist frequency; e) selecting between said second peak and said first peak based on proximity to said second true peak location to determine said target location;

16. 16. The method of claim 15, A method, wherein the pre-set Doppler shift is selected based on at least one of ego-velocity, previous frame information, and neighboring point information.

17. 10. The method of claim 1, further comprising: The at least one up-chirp frequency and the at least one down-chirp frequency are mixed with the one or more return signals to generate a baseband signal in the frequency domain, where the at least one down-chirp frequency is delayed in time proportional to the relative motion.

18. 20. The method of claim 17, The baseband signal includes the first set of peaks and the second set of peaks.

19. A frequency modulated continuous wave (FMCW) light detection and ranging (LIDAR) system (hereinafter referred to as the LIDAR system), comprising: An optical scanner comprising: an optical scanner for transmitting one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency toward a target within a field of view of the LIDAR system, the optical beams being phase modulated such that the at least one up-chirp and the at least one down-chirp have a phase nonlinearity; and receiving one or more return signals based on the one or more optical beams from the target, the optical scanner comprising: the one or more return signals include a displacement up-chirp frequency shifted from at least one up-chirp frequency by at least one relative motion of the target and the LIDAR system, and a displacement down-chirp frequency shifted from at least one down-chirp frequency by at least one relative motion of the target and the LIDAR system, the displacement up-chirp frequencies and the displacement down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; an optical processor coupled to the optical scanner for generating from the return signal a time domain baseband signal including a frequency corresponding to a LIDAR target distance; a signal processing device coupled to the optical processing device, the signal processing device including a processor and a memory storing instructions that, when executed by the processor, operate the LIDAR system to determine the target location using the first set of peaks and the second set of peaks while detecting a peak image based at least in part on shapes of the first set of peaks and the second set of peaks caused by a phase nonlinearity in the one or more light beams; A LIDAR system comprising:

20. 1. A non-transitory computer-readable medium comprising instructions that, when executed by a processor in a Frequency Modulated Continuous Wave (FMCW) Light Detection and Ranging (LIDAR) system (hereinafter referred to as a LIDAR system), cause the LIDAR system to: a) transmit, toward a target within a field of view of the LIDAR system, one or more optical beams including at least one up-chirp frequency and at least one down-chirp frequency, the optical beams being phase modulated such that the at least one up-chirp and the at least one down-chirp have a phase nonlinearity; b) receiving one or more return signals from said target based on said one or more light beams; wherein the one or more return signals include a displacement up-chirp frequency shifted from at least one up-chirp frequency due to at least one relative motion of the target and the LIDAR system, and a displacement down-chirp frequency shifted from at least one down-chirp frequency due to at least one relative motion of the target and the LIDAR system, the displacement up-chirp frequencies and the displacement down-chirp frequencies producing a first set of peaks associated with the at least one up-chirp frequency corresponding to a position of the target, and a second set of peaks associated with the at least one down-chirp frequency corresponding to a position of the target; and c) using the first set of peaks and the second set of peaks to determine the target location while detecting a peak image based at least in part on shapes of the first set of peaks and the second set of peaks caused by phase nonlinearities in the one or more light beams.

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