Method for extracting long-distance single-photon laser radar signal

Abstract

The invention provides a long-distance single-photon laser radar signal extraction method. The method comprises the steps of single laser radar original measurement data stream input, range gate denoising, double-phase histogram target coarse detection, secondary subdivision histogram target fine detection and detection result output. The method provided by the invention is high in reliability, simple in process, small in calculation power and high in algorithm precision, can realize sub-meter detection precision under a hundred kilometer distance, is suitable for an embedded real-time processing system, solves the problem that a target signal is interfered by noise when a space long-distance single-photon laser radar signal is extracted, and improves the detection precision. And the problems of low target detection rate and poor measurement precision are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser radar signal processing, and in particular relates to a method for extracting long-distance single-photon laser radar signals based on photon correlation technology. Background Art

[0002] In response to the demand for long-distance detection of space targets, laser radar has become one of the main distance measurement sensors for satellite platforms with its small size, light weight, high accuracy and fast response speed. The measurement principle of long-distance laser radar is: pulse time of flight (TOF), that is, the distance is measured by measuring the time interval between the laser pulse emission time and the detector detecting the photon. In order to measure long-distance targets, it is generally achieved by increasing the energy of the emitted laser pulse and increasing the sensitivity of the detector. For lasers, low-repetition-rate, high-energy pulsed lasers are generally used. For example, for targets at a distance of 100 kilometers, the laser pulse energy is in the order of tens of millijoules. For detectors, there are two main mechanisms: the first, linear LM-APD, mainly realizes the extraction of return light signals through threshold comparison; the second, Geiger GM-APD, mainly realizes the measurement of return light signals at the single photon level through the avalanche amplification mechanism. The sensitivity of GM-APD detectors can be increased by 3 orders of magnitude compared with LM-APD detectors, and it is the future direction of long-distance laser ranging radars.

[0003] The single-photon laser radar based on GM-APD is quite different from the traditional laser radar in the extraction of effective signals and data processing. GM-APD laser radar mainly extracts effective signals by counting echo photons. The background noise of the detector itself, the background illumination noise of space, and the sun stray light noise reflected by the target may all produce photon counts, thus affecting the extraction of effective signals. Therefore, it is necessary to accumulate the echo signals of multiple photons in the algorithm to obtain a signal with a certain signal-to-noise ratio to capture and track the target. The traditional histogram accumulation algorithm has jumps at the interval boundary; at the same time, if the histogram interval step size is too large, the algorithm solution result is easily affected by noise and the accuracy is not high. If the histogram interval step size is too small, the signal is submerged in the noise and is easy to misjudge. Therefore, how to extract long-distance single-photon laser radar signals with high reliability and high accuracy is an urgent problem to be solved in engineering. Summary of the invention

[0004] In order to overcome the deficiencies in the prior art, the inventors have conducted intensive research and provided a method for extracting long-distance single-photon lidar signals to solve the problem that the target signal is interfered with by noise when extracting long-distance single-photon lidar signals in space, resulting in low target detection rate and poor measurement accuracy.

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

[0006] In a first aspect, a method for extracting a long-distance single-photon laser radar signal comprises:

[0007] S1, selects the latest N laser pulse measurement results of the single-photon laser radar to form a one-dimensional array arry1;

[0008] S2, threshold judgment is performed on array arry1. If the measurement result is within the interval (PV / 2, P+V / 2), it is retained. Otherwise, it is removed as an outlier to obtain 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, set histogram Figure 1 The starting distance is hist1_start, the step size is hist1_step, the number of pre-open intervals is hist1_num, and the number of counts in each interval is hist1_section_cnts; Figure 2 The starting distance is hist2_start, the step length is hist2_step, the number of pre-open intervals is hist2_num; the number of counts in each interval is hist2_section_cnts;

[0011] S3b, set hist1_start = min(array2), that is, histogram Figure 1 The starting distance is the minimum value of array 2; hist1_step = hist2_step, that is, the histogram Figure 1 The step size is equal to the histogram Figure 2 The step length is recorded as step; hist1_num = hist2_num, that 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 recorded as num; hist1_section_cnts = hist1_section_cnts = 0, that is, the histogram Figure 1 , Histogram Figure 2 The number of counts in each interval of is initialized to 0; Figure 2 The starting distance is: hist2_start = hist1_start + step / 2;

[0012] S3c, traverse each data in arry2, and determine whether each data is in the histogram Figure 1 , Histogram Figure 2 The interval in which the data falls, the corresponding interval count number increases by 1;

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

[0014] S3e, select the larger value of hist1_section_cnts_max and hist2_section_cnts_max. If the value is greater than or equal to the threshold value, it is considered that the radar signal has a target, and the distance average value hist_mean of the corresponding interval of the corresponding histogram is determined, which is the rough distance value of the target; if the value is less than the threshold value, it is considered that there is no target in the radar signal; if step S3e determines that the radar signal has a target, execute step S4, otherwise end and do not execute step S4;

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

[0016] S4a, construct the secondary subdivision histogram histThin, set the starting distance to histThin_start, the step length to histThin_step, the number of pre-opened intervals to histThin_num, and the number of counts in each interval to histThin_section_cnts;

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

[0018] S4c, thresholding array 2, if the measurement result is within the interval (hist_mean-step / 2, hist_mean+step / 2), it is retained, otherwise it is removed as an outlier, and then array arry 3 is obtained;

[0019] S4d, traverse each data in arry 3, determine the interval where each data is located in the secondary subdivision histogram histThin, and each time the data falls into an interval, the corresponding interval count number is increased by 1;

[0020] S4e, determine the interval where the maximum value of histThin_section_cnts is located: MaxCntsID;

[0021] S4f, determine the fine interval, set the fine interval half span to kk, the fine interval start histThinDist1, and the fine interval end histThinDist2;

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

[0023] histThinDist2=histThin_start+(MaxCntsID+kk)*histThin_step;S4g, threshold the array3, if the measurement result is within the interval (histThinDist1, histThinDist2), it is retained, otherwise it is removed as an outlier, and the array arry4 is obtained;

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

[0025] In a second aspect, a device for extracting a long-distance single-photon laser radar signal comprises:

[0026] one or more processors;

[0027] a 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 method for extracting long-distance single-photon lidar signals described in the first aspect.

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

[0030] In a fourth aspect, a computer program product is provided, comprising: a computer program (also referred to as code, or instruction), which, when executed, executes the method for extracting long-range single-photon lidar signals described in the first aspect.

[0031] According to a method for extracting a long-distance single-photon laser radar signal provided by the present invention, the following

[0032] Beneficial effects:

[0033] (1) The present invention provides a method for extracting long-distance single-photon laser radar signals, which achieves high-precision distance measurement of the target while ensuring high-reliability detection of the target through coarse detection of the dual-phase histogram target and fine detection of the secondary subdivision histogram target, thereby solving the problems of low detection rate and poor accuracy in the existing methods.

[0034] (2) The present invention provides a method for extracting long-distance single-photon lidar signals and proposes a dual-phase histogram for rough target detection. By simultaneously using two histograms, the two histograms have the same number of intervals and step sizes, and only the starting distance differs by 0.5 steps. This can effectively solve the problem of signal jumping 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 convenient for real-time processing on embedded systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of a method for extracting long-distance single-photon laser radar signals of the present invention;

[0036] Figure 2 Schematic diagram of the rough detection of dual-phase histogram targets;

[0037] Figure 3 This is an example diagram of the rough detection result of the dual-phase histogram target;

[0038] Figure 4 This is an example of the precise detection result of the secondary subdivision histogram target. DETAILED DESCRIPTION

[0039] The following detailed description of the present invention will make the features and advantages of the present invention more clear and explicit.

[0040] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0041] The present invention provides a method for extracting long-distance single-photon laser radar signals, such as Figure 1 As shown, the following steps are included:

[0042] (1) Single LiDAR raw measurement data stream input:

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

[0044] (2) Range gate denoising:

[0045] The array arry1 of step (1) is thresholded. If the measurement result is within the range of (PV / 2, P+V / 2), it is retained. Otherwise, it is removed as an outlier, and then the array arry2 is obtained. Among them, P is the predicted distance value, and V is the distance prediction accuracy. The units of P and V are both m;

[0046] (3) Dual-phase histogram target coarse detection:

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

[0048] (3b) Let hist1_start = min(array2), that is, histogram Figure 1 The starting distance is the minimum value of array2; hist1_step = hist2_step, that is, the histogram Figure 1 The step size is equal to the histogram Figure 2 The step length is recorded as step; hist1_num = hist2_num, that 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 recorded as num; hist1_section_cnts = hist1_section_cnts = 0, that is, the histogram Figure 1 , Histogram Figure 2 The number of counts in each interval of is initialized to 0; Figure 2 The starting distance is: hist2_start = hist1_start + step / 2;

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

[0050] (3c) Traverse each data in arry2 and determine whether each data is in the histogram Figure 1 , Histogram Figure 2 The interval in which the data falls, the corresponding interval count number increases by 1;

[0051] (3d) Determine the histogram Figure 1 The maximum value of the count number of each interval hist1_section_cnts hist1_section_cnts_max, histogram Figure 2 The maximum value of the count number of each section of 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 of hist1_section_cnts_max and hist2_section_cnts_max. If the value is greater than or equal to the threshold value, it is considered that the radar signal has a target, and the distance average value hist_mean of the corresponding interval of the corresponding histogram is determined, which is the rough distance value of the target; if the value is less than the threshold value, it is considered that there is no target in the radar signal. Preferably, the threshold value is 20 to 60, such as 40.

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

[0054] (4a) Construct the secondary subdivision histogram histThin, set the starting distance to histThin_start, the step length to histThin_step, the number of pre-opened intervals to histThin_num, and the number of counts in each interval to histThin_section_cnts;

[0055] Preferably, the step length of the secondary subdivision histogram is histThin_step=0.5-2, such as 0.5 meter; the number of pre-open intervals is histThin_step=200-800, such as 400.

[0056] (4b) Starting distance of secondary subdivision histogram: histThin_start = hist_mean-step / 2; the number of counts in each section is preset to 0, that is, histThin_section_cnts = 0;

[0057] (4c) Perform threshold judgment on array2. If the measurement result is within the interval (hist_mean-step / 2, hist_mean+step / 2), it is retained. Otherwise, it is removed as an outlier, and then array arry3 is obtained.

[0058] (4d) Traverse each data in arry3 and determine the interval where each data is located in the secondary subdivision histogram histThin. Each time the data falls into an interval, the corresponding interval count number is increased by 1;

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

[0060] (4f) Determine the fine interval, set the fine interval half span to kk, the fine interval start histThinDist1, and the fine interval end 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=1-5, such as 2.

[0064] (4g) Perform threshold judgment on array3. If the measurement result is within the interval (histThinDist1, histThinDist2), it is retained. Otherwise, it is removed as an outlier, thereby obtaining array arry4.

[0065] (4h) The array arry4 is averaged 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-distance single-photon laser radar signals can be implemented by a software product. To this end, the present invention also provides a device for extracting long-distance single-photon laser radar signals, including:

[0067] one or more processors;

[0068] a 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-mentioned 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-mentioned method for extracting long-distance single-photon lidar signals.

[0071] The readable storage medium includes, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

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

[0073] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0074] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and product can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0076] Example 1

[0077] A method for extracting long-distance single-photon laser radar signals comprises the following steps:

[0078] (1) Single laser radar raw measurement data stream input: Each laser pulse of the single-photon laser radar has multiple ranging results, and the number of them is different. The measurement results of the latest N (N = 200) laser pulses of the single-photon laser radar are selected to form a one-dimensional array arry1;

[0079] (2) Range gate denoising: The array arry1 of step (1) is threshold judged. If the measurement result is within the interval (PV / 2, P+V / 2), it is retained. Otherwise, it is removed as an outlier, and then the array arry2 is obtained. Among them, P is the predicted distance value, which is obtained a priori; V is the distance prediction accuracy, V=6000 meters, so the threshold judgment interval is (P-3000, P+3000).

[0080] (3) Rough detection of dual-phase histogram targets, as shown in the schematic diagram Figure 2 shown.

[0081] (3a) Construct two histograms. Let Figure 1 The starting distance is hist1_start, the step size is hist1_step, the number of pre-open intervals is hist1_num, and the number of counts in each interval is hist1_section_cnts; Figure 2 The starting distance is hist2_start, the step length is hist2_step, the number of pre-open intervals is hist2_num; the number of counts in 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, that is, hist1_start = min (array2), histogram Figure 2 The starting distance is: hist2_start = hist1_start + step / 2 = min(array2) + 50 meters;

[0083] (3c) Traverse each data in arry 2 and determine whether each data is in the histogram. Figure 1 , Histogram Figure 2 The interval in which the data falls is incremented by 1 for each interval in which the data falls. 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] Among them, iter is the number of data in array arry2, and floor means rounding down.

[0091] (3d) Figure 3 As shown in the figure, it is an example of the rough detection result of the dual-phase histogram target. Figure 1 The maximum value of the count number of each section hist1_section_cnts hist1_section_cnts_max=50, histogram Figure 2 The maximum value of the count number of each section of hist2_section_cnts is hist2_section_cnts_max=66, and hist1_section_cnts and hist2_section_cnts are arrays of length num.

[0092] (3e) Select the larger value of hist1_section_cnts_max and hist2_section_cnts_max. If the value is greater than or equal to the threshold threshold (threshold = 40), it is considered that the radar signal has a target. The distance average value hist_mean of the corresponding interval of the corresponding histogram is calculated, which is the rough distance value of the target; if the value is less than the threshold threshold, it is considered that there is no target in the radar signal. The larger value of hist1_section_cnts_max and hist2_section_cnts_max is 66, which is greater than the threshold threshold (threshold = 40), and it is considered that the radar signal has a target. The rough distance value of the target hist_mean = 53107.65 meters.

[0093] (4) Secondary subdivision histogram target precision detection. Figure 4 The figure shows an example of the precise detection result of the secondary subdivision histogram target.

[0094] (4a) Construct the secondary subdivision histogram histThin, set the starting distance to histThin_start, the step length to histThin_step, the number of pre-opened intervals to histThin_num, and the number of counts in each interval to histThin_section_cnts;

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

[0096] (4c) Perform threshold judgment on array2. If the measurement result is within the interval (hist_mean-step / 2, hist_mean+step / 2), it is retained. Otherwise, it is removed as an outlier, and then array arry3 is obtained.

[0097] (4d) Traverse each data in arry3 and determine the interval where each data is located in the secondary subdivision histogram histThin. Each time the data falls into an interval, the corresponding interval count number is increased by 1. The specific calculation method can 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 a fine interval, where the half span of the fine interval is kk (kk=2), the start of the fine interval is histThinDist1, and the end of the fine interval is 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 interval (histThinDist1, histThinDist2), i.e., (53093.14 meters, 53095.64 meters), it is retained. Otherwise, it is removed as an outlier, thereby obtaining array arry4.

[0103] (4h) The array arry4 is averaged to obtain the high-precision distance measurement result of the target, which is 53094.18 meters, which is the radar target distance measurement result.

[0104] The present invention has been described in detail above in conjunction with specific implementations and exemplary examples, but these descriptions cannot be understood as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, a variety of equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its implementation methods, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be subject to the attached claims.

[0105] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for extracting long-distance single-photon laser radar signals, characterized in that: include: S1, selects the latest N laser pulse measurement results of the single-photon laser radar to form a one-dimensional array arry1; S2, threshold judgment is performed on array arry1. If the measurement result is within the interval (PV / 2, P+V / 2), it is retained. Otherwise, it is removed as an outlier to obtain array arry2; where P is the predicted distance value and V is the distance prediction accuracy; S3, dual-phase histogram target coarse detection, including: S3a, construct two histograms, set the starting distance of histogram 1 to hist1_start, the step size to hist1_step, the number of pre-open intervals to hist1_num, and the number of counts in each interval to hist1_section_cnts; the starting distance of histogram 2 to hist2_start, the step size to hist2_step, the number of pre-open intervals to hist2_num; the number of counts in each interval to hist2_section_cnts; S3b, set hist1_start = min(array2), that is, the starting distance of histogram 1 is the minimum value of array 2; hist1_step = hist2_step, that is, the step length of histogram 1 is equal to the step length of histogram 2, recorded as step; hist1_num = hist2_num, that is, the number of pre-open intervals of histogram 1 is equal to the number of pre-open intervals of histogram 2, recorded as num; hist1_section_cnts = hist1_section_cnts = 0, that is, the number of counts in each interval of histogram 1 and histogram 2 is initialized to 0; the starting distance of histogram 2 is: hist2_start = hist1_start + step / 2; S3c, traverse each data in arry2, determine the interval where each data is located in histogram 1 and histogram 2, and increase the corresponding interval count by 1 each time the data falls into an interval; S3d, determining the maximum value hist1_section_cnts_max of the number of counts of each interval of histogram 1 hist1_section_cnts, and the maximum value hist2_section_cnts_max of the number of counts of each interval of histogram 2 hist2_section_cnts, where hist1_section_cnts and hist2_section_cnts are arrays of length num; S3e, select the larger value of hist1_section_cnts_max and hist2_section_cnts_max. If the value is greater than or equal to the threshold value, it is considered that the radar signal has a target, and the distance average value hist_mean of the corresponding interval of the corresponding histogram is determined, which is the rough distance value of the target; if the value is less than the threshold value, it is considered that there is no target in the radar signal; if step S3e determines that the radar signal has a target, execute step S4, otherwise end and do not execute step S4; S4, secondary subdivision histogram target precision detection, including: S4a, construct the secondary subdivision histogram histThin, set the starting distance to histThin_start, the step length to histThin_step, the number of pre-opened intervals to histThin_num, and the number of counts in each interval to histThin_section_cnts; S4b, starting distance of secondary subdivision histogram: histThin_start=hist_mean-step / 2; the number of counts in each section is preset to 0, that is, histThin_section_cnts=0; S4c, thresholding array2, if the measurement result is within the interval (hist_mean-step / 2, hist_mean+step / 2), it is retained, otherwise it is removed as an outlier, and then array arry3 is obtained; S4d, traverse each data in arry3, determine the interval where each data is located in the secondary subdivision histogram histThin, and each time the data falls into an interval, the corresponding interval count number is increased by 1; S4e, determine the interval where the maximum value of histThin_section_cnts is located: MaxCntsID; S4f, determine the fine interval, set the fine interval half span to kk, the fine interval start histThinDist1, and the fine interval end histThinDist2; histThinDist1=histThin_start+(MaxCntsID-1-kk)*histThin_step; histThinDist2=histThin_start+(MaxCntsID+kk)*histThin_step; S4g, thresholding array3, if the measurement result is within the interval (histThinDist1, histThinDist2), it is retained, otherwise it is removed as an outlier, and array arry4 is obtained; S4h, calculates the average of the 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-distance single-photon laser radar signals according to claim 1, characterized in that: In step S1, in the step of selecting the measurement results of the latest N laser pulses of the single-photon laser radar, the value range of N is 20 to 400.

3. The method for extracting long-distance single-photon laser radar signals according to claim 1, characterized in that: In step S3b, the step length of histogram 1 and histogram 2 is step=50-200 meters; the number of pre-open intervals of histogram 1 and histogram 2 is num=200-800.

4. The method for extracting long-distance single-photon laser radar signals according to claim 1, characterized in that: In step 3e, the threshold value is 20-60.

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

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

7. A device for extracting long-distance single-photon laser radar signals, characterized in that: include: one or more processors; a 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 one of claims 1 to 6.

8. A readable storage medium, characterized in that: A computer program is stored thereon, which, when executed by a processor, implements the method for extracting long-distance single-photon lidar signals as described in one of claims 1 to 6.

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

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