A method for extracting laser ranging signals based on multi-frame superposition

Abstract

The present invention discloses a method for extracting laser ranging signals based on multi-frame superposition, comprising the following steps: constructing an observation sequence; preprocessing and grouping the observation sequence to obtain a template group and a comparison group; equally dividing the template group and the comparison group respectively to obtain a plurality of template group subsequences and a plurality of comparison group subsequences; performing columnar fusion processing on the plurality of template group subsequences to obtain a template group fusion set; and sequentially identifying the plurality of comparison group subsequences according to the template group fusion set to obtain laser ranging signals. The present invention realizes the extraction of laser ranging signals with relatively high accuracy under the conditions of low signal-to-noise ratio and deviation in predicted data.

Description

Technical Field

[0001] The present invention relates to the field of laser ranging for space targets, and specifically, to a method for extracting laser ranging signals based on multi-frame superposition. Background Art

[0002] Laser ranging is a high-precision means for observing space targets. By emitting lasers and recording the time of flight of laser pulses, the distance can be measured.

[0003] Laser ranging technology is currently the method with the highest ranging accuracy for space targets and has important applications in many fields such as satellite orbit determination and the establishment of the Earth reference frame. Due to the uncertainty of the orbits of space targets, the ground station environment, and the system state, all of which can affect the measurement of ranging signals, problems such as a decrease in signal-to-noise ratio and signal bending occur in the ranging signals. Therefore, it is very important to extract laser ranging signals for space targets.

[0004] Currently, scientists in various countries have obtained some research results on the signal extraction of laser ranging observation data, mainly reflected in the application of algorithms and the ideas of data processing. The observation data of laser ranging consists of echo photons. Generally, after subtracting the observed data from the theoretically calculated distance to obtain the residuals, photon scatter points distributed along a curve that vary with time can be obtained. The most basic method for identifying ranging signals is to use a histogram combined with Poisson probability analysis. This method is simple to calculate and can quickly obtain results, and is basically applicable to most situations. However, when signal interruptions occur or the noise level is similar to the signal level, the effect of the Poisson probability analysis algorithm deteriorates. In 2016, Rodriguez, Appleby, and others proposed an improved Poisson filtering algorithm. They provided the necessary conditions for applying Poisson filtering by first attenuating the echo photons to a level close to single photons and then applying the idea of Poisson filtering. Kirchner at the Graz station proposed a fast identification algorithm. This algorithm uses a sliding window to achieve real-time and fast identification of residual data, improving the speed of signal extraction. However, the design of this algorithm is relatively simple and has high requirements for the intensity and distribution of signals. When the signal weakens or bends, the effect of the algorithm deteriorates. Feng Kaibin and others migrated artificial intelligence and image processing methods to signal extraction, taking the residual map as the image input and the image after signal extraction as the output of the model. Through the idea of computer image processing, they removed the noise in the ranging signal and were able to extract the ranging signal.

[0005] Among the existing signal extraction methods, the difficulties in signal extraction are caused by a low signal-to-noise ratio of the signal and deviations in the predicted data. A low signal-to-noise ratio will cause ranging signal photons to be submerged in noise photons, and deviations in the predicted data will cause the residuals of the ranging signal to bend. Summary of the Invention

[0006] To solve the technical problem that the signal extraction in the prior art has poor signal extraction effect under the conditions of low signal-to-noise ratio and deviation in prediction data, the present invention provides a laser ranging signal extraction method based on multi-frame superposition. The technical solution adopted by the present invention is specifically as follows:

[0007] The present invention provides a laser ranging signal extraction method based on multi-frame superposition, comprising the following steps:

[0008] Construct an observation sequence;

[0009] Preprocess and group the observation sequence to obtain a template group and a comparison group;

[0010] Equalize the template group and the comparison group respectively to obtain a number of template group subsequences and a number of comparison group subsequences;

[0011] Perform columnar fusion processing on the number of template group subsequences to obtain a template group fusion set;

[0012] Identify the number of comparison group subsequences in turn according to the template group fusion set to obtain a laser ranging signal.

[0013] As a preferred solution, the method for constructing an observation sequence includes:

[0014] Emit a number of laser pulses within a preset unit time, and denote the number of laser pulses as , ,… ;

[0015] Detect the number of laser pulses, and label all the detected photons with the same serial number, successively as , ,… , and finally obtain an observation sequence .

[0016] As a preferred solution, the method for preprocessing and grouping the observation sequence to obtain a template group and a comparison group includes:

[0017] Preprocess the observation sequence;

[0018] Divide the observation sequence into two groups according to odd and even numbers, specifically:

[0019] ,

[0020] Select any group of sequences as the template group, and the other group of sequences is the comparison group.

[0021] As a preferred solution, the preprocessing method includes:

[0022] Set a residual interval with a preset width to remove gross errors in the observation sequence.

[0023] As a preferred solution, the method of equally dividing the template group and the comparison group respectively to obtain a number of template group subsequences and a number of comparison group subsequences includes:

[0024] Equally divide the template group and the comparison group respectively, and equally divide the template group and the comparison group into subsequences, where each subsequence contains a preset number of pulses, specifically:

[0025]

[0026]

[0027] where is the number of elements in

[0028] As a preferred solution, the method of performing columnar fusion processing on the number of template group subsequences to obtain a template group fusion set includes:

[0029] Columnarize the number of template group subsequences according to the pulse sequence number, and the same pulse sequence number in each subsequence is used as a column to obtain a template group fusion set;

[0030] When is a template group, the template group fusion set is specifically:

[0031]

[0032] When is a template group, the template group fusion set is specifically:

[0033] .

[0034] As a preferred solution, the method of sequentially identifying the number of comparison group subsequences according to the template group fusion set to obtain a laser ranging signal includes:

[0035] Set parameters related to identification;

[0036] Use the station forecast obtained from the CPF file to generate a ranging residual file from the raw laser ranging data;

[0037] Identify the several comparison group subsequences in the order of pulses, compare the residual points in the ranging residual file with the template, and count the number of points falling within the statistical broadening; when the number of points within the statistical broadening is greater than the preset photon number threshold, it is judged as a signal, otherwise it is judged as noise; retain the signal and remove the noise from the subsequence; repeat the equal division process, column fusion process, and identification process on the retained signal, and end when the preset number of iterations is reached or no noise is removed in a certain iteration, and finally obtain the laser ranging signal.

[0038] As a preferred solution, the parameters related to the identification include the noise probability per pulse , the statistical pulse number , the photon number threshold , the statistical broadening ; where:

[0039]

[0040]

[0041]

[0042]

[0043] Among them, is the number of subsequences included in the template; is the system detection probability; is the accidental control coefficient; is the time deviation; is the distance change rate; is the number of noise photons within a certain broadening, is the number of single-pulse echo photons.

[0044] As a preferred solution, the formula for the number of single-pulse echo photons is:

[0045] .

[0046] Among them, is the detection efficiency of the photodetector, is the single-pulse energy, , are the emission and reception efficiencies of the optical system respectively, is the effective cross-sectional area of the corner reflector, is the slant range of the target, is the effective receiving area of the telescope, is the laser divergence angle, is the divergence angle of the corner reflector, is the atmospheric transmittance, is the attenuation factor.

[0047] As an optimal solution, the method further includes:

[0048] Defining accuracy, precision, recall, and F1 value through a confusion matrix to evaluate the effect of laser ranging signal extraction;

[0049] Among them, the confusion matrix includes true positives, false negatives, false positives, and true negatives; the calculation formulas for accuracy, precision, recall, and F1 value are respectively:

[0050]

[0051]

[0052]

[0053]

[0054] Among them, is the accuracy, is the precision, is the recall, TP is true positives, FN is false negatives, FP is false positives, and TN is true negatives.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] The present invention realizes higher precision for laser ranging data with low signal-to-noise ratio and deviation in prediction by sequentially identifying the several comparison group subsequences according to the template group fusion set.

[0057] The present invention can clearly analyze the advantages and disadvantages between different laser ranging data signal extraction algorithms by formulating a quantitative evaluation method.

[0058] Compared with the traditional calibration method, the present invention has strong scalability and can obtain better signal extraction effects by adjusting the recognition parameters according to different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of a laser ranging signal extraction method based on multi-frame superposition provided in this embodiment;

[0060] Figure 2 is a schematic diagram of a laser ranging signal extraction method based on multi-frame superposition provided in this embodiment;

[0061] Figure 3 is a comparison chart of the extraction effects of the present invention and the prior art provided in this embodiment;

[0062] Figure 4 The Compassi5 residual graph provided by this embodiment;

[0063] Figure 5 The Compassi5 extraction effect diagram provided by this embodiment;

[0064] Figure 6 The Apollo15 residual graph provided by this embodiment;

[0065] Figure 7 The Apollo15 extraction effect diagram provided by this embodiment. Detailed implementation manners

[0066] The accompanying drawings are only for illustrative purposes and should not be construed as limiting the present invention;

[0067] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope protected by the embodiments of the present application.

[0068] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0069] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0070] In addition, in the description of this application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0071] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0072] Embodiment 1

[0073] Please refer to Figure 1 , this embodiment provides a method for extracting a laser ranging signal based on multi-frame superposition, including the following steps:

[0074] S1: Construct an observation sequence;

[0075] In a specific embodiment, the method for constructing an observation sequence includes:

[0076] Emit a plurality of laser pulses within a preset unit time, and denote the plurality of laser pulses as , ,... ;

[0077] Detect the plurality of laser pulses, and mark all the detected photons with the same serial number, successively as , ,... , and finally obtain the observation sequence .

[0078] S2: Preprocess and group the observation sequence to obtain a template group and a comparison group;

[0079] In a specific embodiment, the method for preprocessing and grouping the observation sequence to obtain a template group and a comparison group includes:

[0080] Preprocess the observation sequence;

[0081] Divide the observation sequence into two groups according to odd and even numbers, specifically:

[0082] ,

[0083] Select any one group of sequences as the template group, and the other group of sequences is the comparison group.

[0084] In a specific embodiment, the preprocessing method includes:

[0085] Set a residual interval with a preset width to remove gross errors in the observation sequence.

[0086] S3: Divide the template group and the comparison group into equal parts respectively to obtain a number of template group subsequences and a number of comparison group subsequences;

[0087] In a specific embodiment, the method of dividing the template group and the comparison group into equal parts respectively to obtain a number of template group subsequences and a number of comparison group subsequences includes:

[0088] Divide the template group and the comparison group into equal parts respectively, and divide the template group and the comparison group into subsequences, where each subsequence contains a preset number of pulses, specifically:

[0089]

[0090]

[0091] where is the number of elements in

[0092] S4: Perform columnar fusion processing on the number of template group subsequences to obtain a template group fusion set;

[0093] In a specific embodiment, the method of performing columnar fusion processing on the number of template group subsequences to obtain a template group fusion set includes:

[0094] Arrange the number of template group subsequences in columns according to the pulse sequence number, and the same pulse sequence number in each subsequence is used as a column to obtain a template group fusion set;

[0095] When is the template group, the template group fusion set is specifically:

[0096]

[0097] When is the template group, the template group fusion set is specifically:

[0098] 。

[0099] S5: Identify the number of comparison group subsequences in turn according to the template group fusion set to obtain a laser ranging signal;

[0100] In a specific embodiment, the method of identifying the number of comparison group subsequences in turn according to the template group fusion set to obtain a laser ranging signal includes:

[0101] Set the parameters related to recognition;

[0102] Generate a ranging residual file from the original laser ranging data using the station forecast obtained from the CPF file;

[0103] Identify the several comparison group subsequences in the order of pulses, compare the residual points in the ranging residual file with the template, and count the number of points falling within the statistical broadening; when the number of points within the statistical broadening is greater than the preset photon number threshold, it is judged as a signal, otherwise it is judged as noise; retain the signal and remove the noise from the subsequence; repeat the equal division process, column fusion process, and recognition process on the retained signal, and end when the preset number of iterations is reached or no noise is removed in a certain iteration, and finally obtain the laser ranging signal.

[0104] In a specific embodiment, the parameters related to recognition include the per-pulse noise probability , the statistical pulse number , the photon number threshold , the statistical broadening ; where:

[0105]

[0106]

[0107]

[0108]

[0109] Among them, is the number of subsequences included in the template; is the system detection probability; is the accidental control coefficient; is the time deviation; is the distance change rate; is the number of noise photons within a certain broadening, is the number of single-pulse echo photons.

[0110] In a specific embodiment, the method for extracting a laser ranging signal based on multi-frame superposition further includes:

[0111] Define accuracy, precision, recall, and F1 value through a confusion matrix to evaluate the effect of laser ranging signal extraction;

[0112] Among them, the confusion matrix includes true positives, false negatives, false positives, and true negatives; the calculation formulas for accuracy, precision, recall, and F1 value are respectively:

[0113]

[0114]

[0115]

[0116]

[0117] Among them, is the accuracy rate, is the precision rate, is the recall rate, TP is the true positive, FN is the false negative, FP is the false positive, and TN is the true negative.

[0118] Example 2

[0119] This example can be regarded as an improvement or extended example of Example 1, specifically:

[0120] Please refer to Figure 1 , a method for extracting laser ranging signals based on multi-frame superposition, including the following steps:

[0121] S1: Construct an observation sequence;

[0122] In a specific embodiment, the method for constructing an observation sequence includes:

[0123] Emit a plurality of laser pulses per second, and denote the plurality of laser pulses as , , … ;

[0124] Detect the plurality of laser pulses, and label all the detected photons with the same serial number, successively as , , … , and finally obtain the observation sequence ;

[0125] It should be noted that each laser pulse is an independent observation. The vacant part in time is mainly caused by accidental factors (system temporary failures, weather, etc.) and has no impact on the observed data. Therefore, this part is directly removed. After removing the vacant part, the data becomes relatively continuous, and multi-frame overlapping processing can be performed on the measurement data.

[0126] S2: Preprocess and group the observation sequence to obtain a template group and a comparison group;

[0127] In a specific embodiment, please refer to Figure 2 , the method for preprocessing and grouping the observation sequence to obtain a template group and a comparison group includes:

[0128] Preprocess the observation sequence;

[0129] Divide the observation sequence into two groups according to odd and even numbers, specifically:

[0130] ,

[0131] Select the sequence as the template group, then it is the comparison group.

[0132] In a specific embodiment, the preprocessing method includes:

[0133] Set a residual interval with a preset width to remove gross errors in the observation sequence;

[0134] Specifically, set a residual interval with a width of [-5000ns, 5000ns] to remove gross errors in the observation sequence.

[0135] S3: Divide the template group and the comparison group into equal parts respectively to obtain a number of template group subsequences and a number of comparison group subsequences;

[0136] In a specific embodiment, the method of dividing the template group and the comparison group into equal parts respectively to obtain a number of template group subsequences and a number of comparison group subsequences includes:

[0137] Divide the template group and the comparison group into equal parts respectively, and divide the template group and the comparison group into subsequences, where each subsequence contains a preset number of pulses, specifically:

[0138]

[0139]

[0140] where is the number of elements in;

[0141] It should be noted that when the number of pulses in each sequence is small, the use of information will be restricted. When the number of pulses in the subsequence is guaranteed, the larger, the more conducive to the use of information.

[0142] S4: Perform column fusion processing on the several template group subsequences to obtain a template group fusion set;

[0143] In a specific embodiment, please refer to Figure 2 to perform column fusion processing on the several template group subsequences to obtain a template group fusion set, the method includes:

[0144] Separate the several template group subsequences according to the pulse sequence numbers, with the same pulse sequence numbers in each subsequence as a column, to obtain a template group fusion set;

[0145] Specifically, the template group fusion set is:

[0146] 。

[0147] S5: Identify the several comparison group subsequences in turn according to the template group fusion set to obtain a laser ranging signal;

[0148] It should be noted that within the residual interval range of the signal, the probability of noise photons falling within this residual interval is smaller, the probability of signal photons falling within this residual interval is larger, and with the accumulation of time, the accumulation of signal photons is faster than that of noise photons.

[0149] In a specific embodiment, please refer to Figure 2 , the method for identifying the several comparison group subsequences in turn according to the template group fusion set to obtain a laser ranging signal includes:

[0150] Set the parameters related to identification;

[0151] Use the station forecast obtained from the CPF file to generate a ranging residual file from the original laser ranging data;

[0152] Identify the several comparison group subsequences in the order of pulses, compare the residual points in the ranging residual file with the template, and count the number of points falling within the statistical broadening; when the number of points within the statistical broadening is greater than the preset photon number threshold, it is judged as a signal, otherwise it is judged as noise; retain the signal, remove the noise from the subsequence; repeat the equal division process, column fusion process, and identification process for the retained signal, and end when the preset number of iterations is reached or no noise is removed in a certain iteration, and finally obtain a laser ranging signal.

[0153] In a specific embodiment, the parameters related to identification include the per-pulse noise probability , the statistical pulse number , the photon number threshold , the statistical broadening ; where:

[0154]

[0155]

[0156]

[0157]

[0158] wherein, is the number of subsequences included in the template; is the system detection probability; is the accidental control coefficient; is the time deviation; is the rate of change of distance; is the number of noise photons within a certain broadening, is the number of photons in a single-pulse echo;

[0159] It should be noted that the accidental control coefficient is a parameter that includes the situation where the pulse has no signal due to accidental reasons, such as the detector responding to noise, the pulse being blocked by an obstacle, etc., and usually takes a fixed value. The time deviation refers to the deviation between the actual time and the predicted time of the observed target due to the atmospheric deceleration effect. The existence of the time deviation will cause a temporal change trend in the residual data. When the time deviation does not change significantly, the change trend here is related to the rate of change of the target distance. Using the time deviation multiplied by the maximum value of the rate of change of the satellite distance in one circle as the statistical broadening can effectively reduce the influence of the trend term.

[0160] Through the above improvement, compared with directly dividing the interval for statistics, the statistics after image fusion make full use of the accumulation of signals in time. And after image fusion, it can dynamically identify the curved signals, weakening the influence of the trend change of the signals over time. Multiple iterations ensure that the algorithm has good robustness.

[0161] It should be noted that if each observation data is divided into two categories: signal and noise, the process of laser ranging signal extraction can be analogous to a binary classification process, that is, whether the target is successfully measured in a certain observation. Therefore, the evaluation method of binary classification problems can be used to measure the effect of the laser ranging signal extraction algorithm. In binary classification problems, the confusion matrix is an important tool for evaluating the classification results.

[0162] In a specific embodiment, the method for extracting laser ranging signals based on multi-frame superposition further includes:

[0163] Defining accuracy, precision, recall, and F1 value through the confusion matrix to evaluate the effect of laser ranging signal extraction;

[0164] wherein, the confusion matrix includes true positives, false negatives, false positives, and true negatives; the calculation formulas for accuracy, precision, recall, and F1 value are respectively:

[0165]

[0166]

[0167]

[0168]

[0169] Among them, is the accuracy rate, is the precision rate, is the recall rate, TP is the true positive, FN is the false negative, FP is the false positive, and TN is the true negative.

[0170] Specifically, please refer to Figure 3 the result of the F1 value comparison obtained by using the above evaluation method. Figure 3 The step size of the data points in

[0171] is 1 dB. The curve is the method proposed by the present invention, and the straight line is the histogram statistics method. It can be seen that for lunar surface targets, when the signal-to-noise ratio is low but the signal is relatively concentrated, the effect of histogram statistics is limited. The multi-frame superposition method has obvious advantages when the signal-to-noise ratio is greater than -10 dB and can better extract the signal. The precision rate of the multi-frame superposition method is higher than that of the histogram statistics method. Figure 4 and Figure 5 as well as Figure 6 and Figure 7 , the pictures show the results before and after signal extraction for different targets on different dates. It can be seen that the present invention has a higher precision rate for laser ranging data with low signal-to-noise ratio and prediction deviation, and can adjust parameters according to different environments to obtain better signal extraction effects.

[0172] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. A laser ranging signal extraction method based on multi-frame superposition, characterized in that: The following steps are involved: Construct observation sequences; Preprocessing and grouping the observed sequences to obtain template groups and comparison groups; The template group and the comparison group are equally divided to obtain a plurality of template group subsequences and a plurality of comparison group subsequences; Performing column-by-column fusion processing on the plurality of template group subsequences to obtain a template group fusion set; The plurality of comparison group subsequences are identified in sequence according to the template group fusion set to obtain a laser ranging signal; The method of preprocessing and grouping the observed sequences to obtain template groups and comparison groups includes: Preprocessing the observation sequence; The observation sequence is divided into two groups according to odd and even numbers, specifically: ， Select any set of sequences as the template set and the other set of sequences as the comparison set.

2. The method for extracting laser ranging signals based on multi-frame superposition according to claim 1, characterized in that: Methods for constructing observation sequences include: A number of laser pulses are emitted within a preset unit time, and the number of laser pulses are recorded as , ,… ; The plurality of laser pulses are detected, and the detected photons corresponding to the same emission time are all marked with the same serial number, which is , ,… , and finally obtain the observation sequence .

3. The method for extracting laser ranging signals based on multi-frame superposition according to claim 1, characterized in that: The pretreatment method comprises: Set a residual interval of a preset width to remove gross errors in the observation series.

4. The method for extracting laser ranging signals based on multi-frame superposition according to claim 1, characterized in that: The method of equally dividing the template group and the comparison group to obtain a plurality of template group subsequences and a plurality of comparison group subsequences comprises: The template group and the comparison group are divided into two groups: subsequences, each of which contains a preset number of pulses, specifically: in for The number of elements in .

5. The method for extracting laser ranging signals based on multi-frame superposition according to claim 1, characterized in that: The method of performing column-by-column fusion processing on the plurality of template group subsequences to obtain a template group fusion set includes: The plurality of template group subsequences are sorted according to pulse numbers, and the same pulse numbers in each subsequence are taken as one column to obtain a template group fusion set; when When it is a template group, the template group fusion set is specifically: when When it is a template group, the template group fusion set is specifically: 。 6. The method for extracting laser ranging signals based on multi-frame superposition according to claim 1, characterized in that: The method of sequentially identifying the plurality of comparison group subsequences according to the template group fusion set to obtain a laser ranging signal includes: Set identification related parameters; Using the station forecast obtained from the CPF file, the ranging residual file is generated from the laser ranging raw data; The plurality of comparison group subsequences are identified according to the order of pulses, the residual points in the ranging residual file are compared with the template, and the number of points falling within the statistical broadening is counted; when the number of points within the statistical broadening is greater than a preset photon number threshold, it is judged as a signal, otherwise it is judged as noise; the signal is retained, and the noise is removed from the subsequence; the retained signal is repeatedly subjected to equal division processing, column fusion processing, and identification processing, and the process is terminated when a preset number of iterations is reached or no noise is removed in a certain iteration, so as to finally obtain a laser ranging signal.

7. The method for extracting laser ranging signals based on multi-frame superposition according to claim 6, characterized in that: The identification-related parameters include the noise probability per pulse , count the number of pulses , Photon number threshold , Statistical Broadening ;in: in, is the number of subsequences contained in the template; is the system detection probability; is the accident control coefficient; is the time deviation; is the distance change rate; is the number of noise photons within a certain stretch, is the number of single pulse echo photons.

8. The method for extracting laser ranging signals based on multi-frame superposition according to claim 7, characterized in that: The single pulse echo photon number The calculation formula is: in, is the photodetector detection efficiency, is the single pulse energy, , are the transmitting and receiving efficiencies of the optical system, is the effective cross-sectional area of ​​the corner reflector, is the slant range to the target, is the effective receiving area of ​​the telescope, is the laser divergence angle, is the divergence angle of the corner reflector, is the atmospheric transmittance, is the attenuation factor, λ is the laser wavelength, h is the Planck constant, and c is the speed of light.

9. A laser ranging signal extraction method based on multi-frame superposition according to any one of claims 1 to 8, characterized in that: The method further comprises: The accuracy, precision, recall and F1 value are defined through confusion matrix to evaluate the effect of laser ranging signal extraction; The confusion matrix includes true positive examples, false negative examples, false positive examples and true negative examples; the calculation formulas for the accuracy, precision, recall and F1 value are: in, is the accuracy, is the accuracy, is the recall rate, TP is a true positive example, FN is a false negative example, FP is a false positive example, and TN is a true negative example.

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