A method for extracting a remote single-photon lidar signal

Abstract

The application provides a method for extracting a remote single-photon laser radar signal, comprising: single laser radar original measurement data stream input, distance gate denoising, double-phase histogram target rough detection, secondary subdivided histogram target fine detection, and detection result output. The method provided by the application has high reliability, a simple process, small computing power, and high algorithm accuracy, can realize sub-meter detection accuracy at a distance of 100 kilometers, and is suitable for embedded real-time processing systems, thereby solving the problem that target signals are disturbed by noise during space remote single-photon laser radar signal extraction, resulting in low target detection rate and poor measurement accuracy.

Description

Technical Field

[0001] This invention belongs to the field of lidar signal processing technology, and specifically relates to a method for extracting long-range single-photon lidar signals based on photon correlation technology. Background Technology

[0002] To meet the need for long-range detection of space targets, lidar, with its small size, light weight, high precision, and fast response speed, has become one of the main range measurement sensors on spaceborne platforms. The measurement principle of long-range lidar is the Time-of-Flight (TOF) method, which measures the distance by measuring the time interval between the laser pulse emission and the time it takes for the detector to detect the photon. To measure long-range targets, this is generally achieved by increasing the energy of the emitted laser pulse and improving the sensitivity of the detector. For the laser, low-repetition-rate, high-energy pulsed lasers are typically used; for example, for targets at a distance of 100 kilometers, the laser pulse energy is on the order of tens of millijoules. For the detector, there are two main mechanisms: the first is the linear LM-APD, which mainly extracts the return signal through threshold comparison; the second is the Geiger GM-APD, which mainly uses an avalanche amplification mechanism to measure the return signal at the single-photon level. The GM-APD detector offers a sensitivity improvement of three orders of magnitude compared to the LM-APD detector and represents the future direction for long-range lidar ranging radar.

[0003] Single-photon lidar based on GM-APD differs significantly from traditional lidar in its effective signal extraction and data processing. GM-APD lidar primarily extracts the effective signal by counting echo photons. However, background noise from the detector itself, ambient light noise, and stray sunlight reflected from the target can all affect photon counting, thus impacting effective signal extraction. Therefore, the algorithm needs to accumulate the echo signals from multiple photons to obtain a signal with a certain signal-to-noise ratio for target acquisition and tracking. Traditional histogram accumulation algorithms suffer from jumps at interval boundaries; furthermore, if the histogram interval step size is too large, the algorithm's solution is easily affected by noise, resulting in low accuracy; if the histogram interval step size is too small, the signal is submerged in noise, leading to misjudgments. Therefore, how to reliably and accurately extract signals from long-range single-photon lidar is a pressing engineering problem that needs to be solved. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the inventors have conducted intensive research and provided a method for extracting long-range single-photon lidar signals, which solves the problem that the target signal is affected by noise interference during the extraction of long-range single-photon lidar signals in space, resulting in low target detection rate and poor measurement accuracy.

[0005] The technical solution provided by this invention is as follows:

[0006] Firstly, a method for extracting signals from a long-range single-photon lidar includes:

[0007] S1, select the measurement results of the latest N laser pulses of the single-photon lidar to form a one-dimensional array arry1;

[0008] S2, perform threshold judgment on array arry1. If the measurement result is within the range of (PV / 2, P+V / 2), it is retained; otherwise, it is discarded as an outlier, resulting in array arry2; where P is the predicted distance value and V is the distance prediction accuracy.

[0009] S3, dual-phase histogram target coarse detection, including:

[0010] S3a, construct two histograms, and set the histograms... Figure 1 The starting distance is hist1_start, the step size is hist1_step, the number of pre-opened intervals is hist1_num, and the number of counts for each interval is hist1_section_cnts; histogram Figure 2 The starting distance is hist2_start, the step size is hist2_step, the number of pre-opened intervals is hist2_num, and the number of counts for each interval is hist2_section_cnts.

[0011] S3b, let hist1_start = min(array2), that is, the histogram. Figure 1 The initial distance is the minimum value of array 2; hist1_step = hist2_step, which is the histogram. Figure 1 The step size is equal to the histogram. Figure 2 The step size is denoted as step; hist1_num = hist2_num, which is the histogram. Figure 1 The number of pre-open intervals is equal to the histogram. Figure 2 The number of pre-open intervals is denoted as num; hist1_section_cnts = hist1_section_cnts = 0, i.e., the histogram... Figure 1 histogram Figure 2 The count of each interval is initialized to 0; the histogram Figure 2 The starting distance is: hist2_start = hist1_start + step / 2;

[0012] S3c iterates through each data point in array2 and determines the histogram for each data point. Figure 1 histogram Figure 2 The interval in which the data falls is incremented by 1 for each interval it falls into.

[0013] S3d, determine histogram Figure 1 The maximum value of the counts in each interval, hist1_section_cnts_max, and histogram. Figure 2 The maximum value of the count of each interval, hist2_section_cnts, is hist2_section_cnts_max. hist1_section_cnts and hist2_section_cnts are arrays of length num.

[0014] In step S3e, the larger of hist1_section_cnts_max and hist2_section_cnts_max is selected. If this value is greater than or equal to the threshold, the radar signal is considered to have a target, and the average distance of the corresponding interval of the histogram, hist_mean, is determined, which is the coarse distance value of the target. If this value is less than the threshold, the radar signal is considered to have no target. If step S3e determines that the radar signal has a target, step S4 is executed; otherwise, the process ends and step S4 is not executed.

[0015] S4, secondary subdivision histogram target fine detection, including:

[0016] S4a, construct a secondary subdivision histogram histThin, with the starting distance as histThin_start, the step size as histThin_step, the number of pre-opened intervals as histThin_num, and the number of counts for each interval as histThin_section_cnts;

[0017] S4b, the starting distance of the second subdivision histogram: histThin_start = hist_mean - step / 2; the number of counts for each interval is preset to 0, that is, histThin_section_cnts = 0;

[0018] S4c performs a threshold check on array2. If the measurement result is within the range of (hist_mean-step / 2, hist_mean+step / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining array array3.

[0019] S4d iterates through each data point in array 3, determines the interval in the secondary subdivision histThin for each data point, and increments the interval count by 1 for each data point that falls into an interval.

[0020] S4e, determine the interval containing the maximum value of histThin_section_cnts: MaxCntsID;

[0021] S4f, determine the fine interval, let the half span of the fine interval be kk, the start of the fine interval is histThinDist1, and the end of the fine interval is histThinDist2;

[0022] histThinDist1=histThin_start+(MaxCntsID-1-kk)*histThin_step;

[0023] histThinDist2 = histThin_start + (MaxCntsID + kk) * histThin_step; S4g, perform threshold judgment on array3. If the measurement result is within the range of (histThinDist1, histThinDist2), it is retained; otherwise, it is discarded as an outlier, resulting in array array4.

[0024] S4h calculates the average of array arry4 to obtain the high-precision ranging result of the target, which is the radar target ranging result.

[0025] Secondly, a device for extracting signals from a long-range single-photon lidar includes:

[0026] One or more processors;

[0027] Storage device for storing one or more programs.

[0028] When the one or more programs are executed by the one or more processors, the one or more processors implement the long-range single-photon lidar signal extraction method described in the first aspect.

[0029] Thirdly, a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for extracting long-range single-photon lidar signals as described in the first aspect.

[0030] Fourthly, a computer program product comprising: a computer program (also referred to as code or instructions) that, when executed, performs the long-range single-photon lidar signal extraction method described in the first aspect.

[0031] The present invention provides a method for extracting long-range single-photon lidar signals, which has the following characteristics:

[0032] Beneficial effects:

[0033] (1) The present invention provides a method for extracting long-range single-photon lidar signals. By using dual-phase histogram target coarse detection and secondary subdivision histogram target fine detection, it achieves high-precision ranging of the target while ensuring high reliability of target detection, thus solving the problems of low detection rate and poor accuracy of existing methods.

[0034] (2) The present invention provides a method for extracting long-range single-photon lidar signals. The proposed dual-phase histogram target coarse detection method uses two histograms simultaneously. The number of intervals and step size of the two histograms are the same, and the starting distances differ by only 0.5 steps. This method can effectively solve the problem of signal jump at the boundary of the histogram interval during the target detection process, and the method has stronger adaptability. The method of the present invention has low complexity and is easy to process in real time on embedded systems. Attached Figure Description

[0035] Figure 1 This is a flowchart of the long-range single-photon lidar signal extraction method of the present invention;

[0036] Figure 2 This is a schematic diagram of coarse target detection using a two-phase histogram.

[0037] Figure 3 This is an example of coarse target detection results using a two-phase histogram.

[0038] Figure 4 This is an example of the target fine detection results from the secondary subdivision histogram. Detailed Implementation

[0039] The features and advantages of the present invention will become clearer and more apparent from the following detailed description.

[0040] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0041] This invention provides a method for extracting signals from long-range single-photon lidar, such as... Figure 1 As shown, it includes the following steps:

[0042] (1) Input of raw measurement data stream from a single lidar:

[0043] Each laser pulse of a single-photon lidar has multiple ranging results, and the number of results is different. The measurement results of the latest N laser pulses of the single-photon lidar are selected to form a one-dimensional array arry1. The value of N ranges from 20 to 400, such as 200.

[0044] (2) Distance gate noise reduction:

[0045] The array arry1 from step (1) is subjected to threshold judgment. If the measurement result is within the range (PV / 2, P+V / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining the array arry2. Here, P is the predicted distance value, and V is the distance prediction accuracy. The units of P and V are both meters.

[0046] (3) Coarse target detection using two-phase histogram:

[0047] (3a) Construct two histograms, and set the histograms... Figure 1 The starting distance is hist1_start, the step size is hist1_step, the number of pre-opened intervals is hist1_num, and the number of counts for each interval is hist1_section_cnts; histogram Figure 2 The starting distance is hist2_start, the step size is hist2_step, the number of pre-opened intervals is hist2_num, and the number of counts for each interval is hist2_section_cnts.

[0048] (3b) Let hist1_start = min(array2), that is, histogram Figure 1 The initial distance is the minimum value of array2; hist1_step = hist2_step, which is the histogram. Figure 1 The step size is equal to the histogram. Figure 2 The step size is denoted as step; hist1_num = hist2_num, which is the histogram. Figure 1 The number of pre-open intervals is equal to the histogram. Figure 2 The number of pre-open intervals is denoted as num; hist1_section_cnts = hist1_section_cnts = 0, i.e., the histogram... Figure 1 histogram Figure 2 The count of each interval is initialized to 0; the histogram Figure 2 The starting distance is: hist2_start = hist1_start + step / 2;

[0049] Preferably, histogram Figure 1 histogram Figure 2 The step length is 50 to 200, such as 100 meters; the histogram... Figure 1 histogram Figure 2 The number of pre-opened intervals num = 200 to 800, such as 400.

[0050] (3c) Iterate through each data point in arry2 and determine the histogram of each data point. Figure 1 histogram Figure 2 The interval in which the data falls is incremented by 1 for each interval it falls into.

[0051] (3d) Determine the histogram Figure 1 The maximum value of the counts in each interval, hist1_section_cnts_max, and histogram. Figure 2 The maximum value of the count of each interval, hist2_section_cnts, is hist2_section_cnts_max. hist1_section_cnts and hist2_section_cnts are arrays of length num.

[0052] (3e) Select the larger value between hist1_section_cnts_max and hist2_section_cnts_max. If this value is greater than or equal to the threshold, the radar signal is considered to contain a target, and the average distance of the corresponding interval in the histogram, hist_mean, is determined, which is the coarse range value of the target. If this value is less than the threshold, the radar signal is considered to contain no target. Preferably, the threshold is 20 to 60, such as 40.

[0053] (4) Secondary subdivision histogram target fine detection: If step (3e) determines that there is a target in the radar signal, this step is executed; otherwise, it is not executed.

[0054] (4a) Construct a secondary subdivision histThin, with the starting distance as histThin_start, the step size as histThin_step, the number of pre-opened intervals as histThin_num, and the number of counts for each interval as histThin_section_cnts;

[0055] Preferably, the step size of the secondary subdivision histogram is histThin_step = 0.5 to 2, such as 0.5 meters; the number of pre-opened intervals is histThin_step = 200 to 800, such as 400.

[0056] (4b) Secondary subdivision histogram starting distance: histThin_start = hist_mean - step / 2; the number of counts for each interval is preset to 0, i.e., histThin_section_cnts = 0;

[0057] (4c) Perform threshold judgment on array2. If the measurement result is within the range of (hist_mean-step / 2, hist_mean+step / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining array3.

[0058] (4d) Iterate through each data in arry3 and determine the interval in the secondary subdivision histThin for each data. For each data that falls into an interval, increment the corresponding interval count by 1.

[0059] (4e) Calculate the interval containing the maximum value of histThin_section_cnts: MaxCntsID;

[0060] (4f) Determine the fine interval. Let the half span of the fine interval be kk, the start of the fine interval be histThinDist1, and the end of the fine interval be histThinDist2.

[0061] histThinDist1=histThin_start+(MaxCntsID-1-kk)*histThin_step;

[0062] histThinDist2=histThin_start+(MaxCntsID+kk)*histThin_step;

[0063] Preferably, the fine interval half-span kk = 1 to 5, such as 2.

[0064] (4g) Perform threshold judgment on array3. If the measurement result is within the range of (histThinDist1, histThinDist2), it is retained; otherwise, it is discarded as an outlier, thus obtaining array array4.

[0065] (4h) Calculate the mean of array arry4 to obtain the high-precision ranging result of the target, which is the radar target ranging result.

[0066] The above-mentioned method for extracting long-range single-photon lidar signals can be implemented using software products. Therefore, this invention also provides a device for extracting long-range single-photon lidar signals, comprising:

[0067] One or more processors;

[0068] Storage device for storing one or more programs.

[0069] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described method for extracting long-range single-photon lidar signals.

[0070] The present invention also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for extracting long-range single-photon lidar signals.

[0071] The readable storage media include, but are not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0072] The present invention also provides a computer program product, the computer program product comprising: a computer program (also referred to as code or instructions), which, when the computer program is run, executes the above-described method for extracting long-range single-photon lidar signals.

[0073] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device and product described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] Example 1

[0077] A method for extracting signals from a long-range single-photon lidar includes the following steps:

[0078] (1) Input of raw measurement data stream of single lidar: Each laser pulse of single photon lidar has multiple ranging results, and the number of results is different; select the measurement results of the latest N (N=200) laser pulses of single photon lidar to form a one-dimensional array arry1;

[0079] (2) Distance gate denoising: The array arry1 from step (1) is thresholded. If the measurement result is within the range (PV / 2, P+V / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining the array arry2. Here, P is the predicted distance value, obtained through prior knowledge; V is the distance prediction accuracy, V = 6000 meters, so the threshold judgment range is (P-3000, P+3000).

[0080] (3) Coarse target detection using dual-phase histogram, as shown in the schematic diagram. Figure 2 As shown.

[0081] (3a) Construct two histograms, and set the histograms... Figure 1 The starting distance is hist1_start, the step size is hist1_step, the number of pre-opened intervals is hist1_num, and the number of counts for each interval is hist1_section_cnts; histogram Figure 2 The starting distance is hist2_start, the step size is hist2_step, the number of pre-opened intervals is hist2_num, and the number of counts for each interval is hist2_section_cnts.

[0082] (3b) hist1_step=hist2_step=step=100 meters, hist1_num=hist2_num=num=400, hist1_section_cnts=hist1_section_cnts=0, histogram Figure 1 The starting distance is the minimum value of array 2, i.e., hist1_start = min(array2), and the histogram... Figure 2 The starting distance is: hist2_start = hist1_start + step / 2 = min(array2) + 50 meters;

[0083] (3c) Iterate through each data point in array 2 and determine the histogram for each data point. Figure 1 histogram Figure 2 For each interval in which the data falls, the interval count is incremented by 1. The calculation method is as follows:

[0084] for i = 1: iter

[0085] id1=floor(arry2(i)-hist1_start) / step;

[0086] id2=floor(arry2(i)-hist2_start) / step;

[0087] hist1_section_cnts(id1)=hist1_section_cnts(id1)+1;

[0088] hist2_section_cnts(id1)=hist2_section_cnts(id2)+1;

[0089] end

[0090] Here, iter is the number of data items in array arry2, and floor indicates rounding down.

[0091] (3d) such as Figure 3 The image shown is an example of coarse target detection results using a two-phase histogram. Figure 1 The maximum value of the counts for each interval, hist1_section_cnts, is hist1_section_cnts_max = 50, and the histogram... Figure 2 The maximum value of the count of each interval, hist2_section_cnts, is hist2_section_cnts_max = 66. hist1_section_cnts and hist2_section_cnts are arrays of length num.

[0092] (3e) Select the larger value between hist1_section_cnts_max and hist2_section_cnts_max. If this value is greater than or equal to the threshold (threshold = 40), the radar signal is considered to have a target. Calculate the average distance of the corresponding interval in the histogram, hist_mean, which is the coarse range value of the target. If this value is less than the threshold, the radar signal is considered to have no target. The larger value between hist1_section_cnts_max and hist2_section_cnts_max is 66, which is greater than the threshold (threshold = 40), so the radar signal is considered to have a target. The coarse range value of the target, hist_mean, is 53107.65 meters.

[0093] (4) Secondary subdivision histogram for precise target detection. For example... Figure 4 The image shown is an example of the target precision detection result using a secondary subdivision histogram.

[0094] (4a) Construct a secondary subdivision histThin, with the starting distance as histThin_start, the step size as histThin_step, the number of pre-opened intervals as histThin_num, and the number of counts for each interval as histThin_section_cnts;

[0095] (4b) histThin_step = 0.5 meters; the starting distance of the second subdivision histogram is histThin_start = hist_mean-step / 2 = 53057.65 meters; the number of counts in each interval is preset to 0, that is, histThin_section_cnts = 0;

[0096] (4c) Perform threshold judgment on array2. If the measurement result is within the range of (hist_mean-step / 2, hist_mean+step / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining array3.

[0097] (4d) Traverse each data in arry3 and determine the interval in the secondary subdivision histThin for each data. For each data that falls into an interval, increment the corresponding interval count by 1. For the specific calculation method, refer to step (3c).

[0098] (4e) Calculate the maximum value of histThin_section_cnts, histThin_section_cntsMax=18, and calculate the interval corresponding to the maximum count value, MaxCntsID=74;

[0099] (4f) Determine the fine interval, the half span of the fine interval is kk (kk=2), the fine interval starts at histThinDist1, and the fine interval ends at histThinDist2;

[0100] histThinDist1=histThin_start+(MaxCntsID-1-kk)*histThin_step=53093.14 meters;

[0101] histThinDist2=histThin_start+(MaxCntsID+kk)*histThin_step=53095.64 meters;

[0102] (4g) Perform threshold judgment on array 3. If the measurement result is within the range of (histThinDist1, histThinDist2) (53093.14 meters, 53095.64 meters), it is retained; otherwise, it is discarded as an outlier, thus obtaining array array4.

[0103] (4h) Calculate the mean of array arry4 to obtain the high-precision ranging result of the target, which is 53094.18 meters, which is the radar target ranging result.

[0104] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

[0105] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for extracting signals from a long-range single-photon lidar, characterized in that, include: S1, select the measurement results of the latest N laser pulses of the single-photon lidar to form a one-dimensional array arry1; S2, perform threshold judgment on array arry1. If the measurement result is within the range of (PV / 2, P+V / 2), it is retained; otherwise, it is discarded as an outlier, resulting in array arry2; where P is the predicted distance value and V is the distance prediction accuracy. S3, dual-phase histogram coarse target detection, including: S3a, construct two histograms. Let the starting distance of histogram 1 be hist1_start, the step size be hist1_step, the number of pre-opened intervals be hist1_num, and the count of each interval be hist1_section_cnts; let the starting distance of histogram 2 be hist2_start, the step size be hist2_step, the number of pre-opened intervals be hist2_num, and the count of each interval be hist2_section_cnts. S3b, let hist1_start = min(array2), meaning the starting distance of histogram 1 is the minimum value of array 2; hist1_step = hist2_step, meaning the step size of histogram 1 is equal to the step size of histogram 2, denoted as step; hist1_num = hist2_num, meaning the number of pre-open intervals of histogram 1 is equal to the number of pre-open intervals of histogram 2, denoted as num; hist1_section_cnts = hist1_section_cnts = 0, meaning the count of each interval in histogram 1 and histogram 2 is initialized to 0; the starting distance of histogram 2 is: hist2_start = hist1_start + step / 2; S3c: Iterate through each data point in array2, determine the interval in histogram 1 and histogram 2 for each data point, and increment the interval count by 1 for each data point that falls into an interval. S3d, determine the maximum value of the count hist1_section_cnts for each interval of histogram 1, hist1_section_cnts_max, and the maximum value of the count hist2_section_cnts for each interval of histogram 2, hist2_section_cnts_max, where hist1_section_cnts and hist2_section_cnts are arrays of length num; In step S3e, the larger of hist1_section_cnts_max and hist2_section_cnts_max is selected. If this value is greater than or equal to the threshold, the radar signal is considered to have a target, and the average distance of the corresponding interval of the histogram, hist_mean, is determined, which is the coarse distance value of the target. If this value is less than the threshold, the radar signal is considered to have no target. If step S3e determines that the radar signal has a target, step S4 is executed; otherwise, the process ends and step S4 is not executed. S4, secondary subdivision histogram target fine detection, including: S4a, construct a secondary subdivision histogram histThin, with the starting distance as histThin_start, the step size as histThin_step, the number of pre-opened intervals as histThin_num, and the number of counts for each interval as histThin_section_cnts; S4b, the starting distance of the second subdivision histogram: histThin_start = hist_mean - step / 2; the number of counts for each interval is preset to 0, that is, histThin_section_cnts = 0; S4c performs a threshold check on array2. If the measurement result is within the range of (hist_mean-step / 2, hist_mean+step / 2), it is retained; otherwise, it is discarded as an outlier, thus obtaining array3. S4d iterates through each data point in arry3 and determines the interval in the secondary subdivision histThin for each data point. For each data point that falls into an interval, the corresponding interval count is incremented by 1. S4e, determine the interval containing the maximum value of histThin_section_cnts: MaxCntsID; S4f, determine the fine interval, let the half span of the fine interval be kk, the start of the fine interval is histThinDist1, and the end of the fine interval is histThinDist2; histThinDist1=histThin_start+(MaxCntsID-1-kk)*histThin_step; histThinDist2=histThin_start+(MaxCntsID+kk)*histThin_step; S4g performs a threshold check on array3. If the measurement result is within the range (histThinDist1, histThinDist2), it is retained; otherwise, it is discarded as an outlier, resulting in array array4. S4h calculates the average of array arry4 to obtain the high-precision ranging result of the target, which is the radar target ranging result.

2. The method for extracting long-range single-photon lidar signals according to claim 1, characterized in that, In step S1, where the measurement results of the latest N laser pulses from the single-photon lidar are selected, the value of N ranges from 20 to 400.

3. The method for extracting long-range single-photon lidar signals according to claim 1, characterized in that, In step S3b, the step size of histogram 1 and histogram 2 is step = 50 to 200 meters; the number of pre-opened intervals of histogram 1 and histogram 2 is num = 200 to 800.

4. The method for extracting long-range single-photon lidar signals according to claim 1, characterized in that, In step 3e, the threshold is set to 20-60.

5. The method for extracting long-range single-photon lidar signals according to claim 1, characterized in that, In step S4b, the step size of the secondary subdivision histogram is histThin_step = 0.5 to 2 meters; the number of pre-opened intervals is histThin_step = 200 to 800.

6. The method for extracting long-range single-photon lidar signals according to claim 1, characterized in that, In step S4b, the fine interval half-span kk = 1 to 5.

7. A device for extracting signals from a long-range single-photon lidar, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for extracting long-range single-photon lidar signals as described in any one of claims 1 to 6.

8. A readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for extracting long-range single-photon lidar signals as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes: a computer program that, when the computer program is run, executes the method for extracting long-range single-photon lidar signals as described in any one of claims 1 to 6.

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