Space-based single-photon lidar space debris ranging method and system

Abstract

The application relates to a space debris ranging method and system of a space-based single-photon laser radar, and the method comprises the following steps: acquiring a first speed and a first acceleration of a first photon; acquiring a second speed and a second acceleration of a second photon; calculating a speed difference according to the first speed and the second speed; calculating an acceleration difference according to the first acceleration and the second acceleration; and clustering all-domain photons according to a sparse photon clustering algorithm, the speed difference and the acceleration difference, and generating a target trajectory. Through the sparse photon clustering algorithm, the correlation between the time-distance of the space debris target motion is utilized to calculate the speed difference and the acceleration difference of the photons in the time neighborhood, then the photons in the whole domain are clustered through the speed difference and the acceleration difference, and finally the accurate target trajectory is obtained. On one hand, the original flight time of the speed search signal photon is searched, and the error caused by the statistical method is avoided; on the other hand, the precision loss and the super-large calculation amount caused by the image gridding are also avoided.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, and in particular to a space-based single-photon lidar method and system for space debris ranging. Background Technology

[0002] In recent years, the number of space debris objects in orbit has increased exponentially, threatening the safety of operational spacecraft and posing a potential collision risk to future spacecraft launches, tests, and other space activities. Satellite laser ranging (SLR) is one of the most effective space debris measurement technologies since the beginning of the new century. Equipped with higher pulse energy and nanosecond pulse lasers, SLR can detect diffusely reflected photons from failed satellites or rocket bodies thousands of kilometers away, with a single-shot accuracy of approximately 1 meter. Existing kilometer-class SLR systems are mostly ground-based, limited by geographical location, atmospheric interference, and limited operating time (within a few hours before and after twilight).

[0003] Space-based SLR systems can overcome the aforementioned limitations, operating 24 / 7 in all weather conditions and offering greater flexibility for non-cooperative objects. However, these systems are limited by finite laser energy and weight. Single-photon avalanche photodiodes (SPADs), with their fast rise time and high single-photon sensitivity, can significantly improve the detection efficiency of single-photon lidar systems and reduce the energy requirements of laser pulses. However, this increased sensitivity to weak photon signals comes at the cost of inducing substantial background noise. High-precision acquisition, tracking, and pointing (ATP) devices, coupled with accurate trajectory prediction information, can mitigate the impact of background noise to some extent and reduce data volume. However, for sparse photon signals submerged in widespread and indistinguishable noise, rapid and accurate data processing methods are urgently needed.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the difficulty in distinguishing sparse photon signals submerged in a wide range of noise in the prior art.

[0006] To address the aforementioned technical problems, a first aspect of the present invention provides a space-based single-photon lidar method for space debris ranging, the method comprising:

[0007] Obtain the first velocity and first acceleration of the first photon;

[0008] To obtain the second velocity and second acceleration of the second photon;

[0009] Calculate the speed difference based on the first speed and the second speed;

[0010] Calculate the acceleration difference based on the first acceleration and the second acceleration;

[0011] The target trajectory is generated by clustering global photons based on the sparse photon clustering algorithm, the velocity difference, and the acceleration difference.

[0012] In one embodiment of the present invention, the step of generating a target trajectory by clustering global photons according to a sparse photon clustering algorithm, the velocity difference, and the acceleration difference further includes:

[0013] If the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then the acceleration difference or the photon corresponding to the velocity difference is added to the set;

[0014] Photons in the set that are greater than the noise threshold and have the same velocity and acceleration are clustered to generate the target trajectory.

[0015] In one embodiment of the present invention, after the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method further includes:

[0016] If the set is empty, the search depth is doubled.

[0017] In one embodiment of the present invention, after the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method further includes:

[0018] If the set is empty and the temporal neighborhood width is equal to the pulse period, then the photon search is terminated.

[0019] In one embodiment of the present invention, the steps of obtaining the first velocity and the first acceleration of the first photon include:

[0020] Acquire the first data of the first photon; the first photon data includes the first time and the first distance;

[0021] Calculate the first velocity and first acceleration of the first photon in the time neighborhood based on the first time and the first distance.

[0022] In one embodiment of the present invention, the step of obtaining the second velocity and the second acceleration of the second photon further includes:

[0023] Acquire the second photon data of the second photon; the second photon data includes a second time and a second distance;

[0024] The second velocity and second acceleration of the second photon within the time neighborhood are calculated based on the second time and the second distance.

[0025] A second aspect of the present invention provides a space-based single-photon lidar space debris ranging system, the system comprising: an acquisition module, a calculation module, and a clustering module;

[0026] The acquisition module is configured to: acquire the first velocity and first acceleration of the first photon; acquire the second velocity and second acceleration of the second photon;

[0027] The calculation module is configured to: calculate the speed difference based on the first speed and the second speed; and calculate the acceleration difference based on the first acceleration and the second acceleration.

[0028] The clustering module is configured to cluster global photons based on a sparse photon clustering algorithm, the velocity difference, and the acceleration difference to generate a target trajectory.

[0029] In one embodiment of the present invention, the clustering module is further configured to:

[0030] If the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then the acceleration difference or the photon corresponding to the velocity difference is added to the set;

[0031] Photons in the set that are greater than the noise threshold and have the same velocity and acceleration are clustered to generate the target trajectory.

[0032] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0033] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0034] The technical solution of the present invention has the following advantages compared with the prior art:

[0035] This invention discloses a space-based single-photon lidar method and system for space debris ranging. It utilizes a sparse photon clustering algorithm, leveraging the time-distance correlation of space debris targets, to calculate the velocity and acceleration differences of photons within the time neighborhood. Then, it clusters photons across the entire domain using these velocity and acceleration differences, ultimately obtaining an accurate target trajectory. This application avoids errors introduced by statistical methods by searching for the original flight time of signal photons based on velocity, and also avoids the accuracy loss and excessive computational load caused by image rasterization. Attached Figure Description

[0036] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0037] Figure 1 This is a flowchart of a space-based single-photon lidar space debris ranging method and system provided by the present invention;

[0038] Figure 2 This invention provides a space-based single-photon lidar method for ranging space debris and a diagram of the ranging data of the original photons in the system.

[0039] Figure 3 This invention provides a bar chart of acceleration values ​​in a space debris ranging method and system for space-based single-photon lidar.

[0040] Figure 4 This invention provides a data diagram of marked signal photons in a space debris ranging method and system for space-based single-photon lidar.

[0041] Figure 5 This invention provides a space-based single-photon lidar method for space debris ranging and a target trajectory diagram of signal photons in the system.

[0042] Figure 6 This is a linear trajectory diagram of a space debris ranging method and system for space-based single-photon lidar provided by the present invention;

[0043] Figure 7 This is a quadratic trajectory diagram of a space debris ranging method and system for space-based single-photon lidar provided by the present invention;

[0044] Figure 8 This is a cubic trajectory diagram of a space debris ranging method and system for space-based single-photon lidar provided by the present invention;

[0045] Figure 9 This invention provides a space-based single-photon lidar method for ranging space debris and a trajectory diagram of two targets in the system.

[0046] Figure 10 This invention provides a space-based single-photon lidar method for ranging space debris and trajectory diagrams of three targets in the system.

[0047] Figure 11 This invention provides a space-based single-photon lidar method for ranging space debris and a trajectory diagram in the system when the SNR is -9.5dB.

[0048] Figure 12 This invention provides a space-based single-photon lidar method for ranging space debris and a trajectory diagram of the system when the SNR is -12.9dB.

[0049] Figure 13 This invention provides a space-based single-photon lidar method for ranging space debris and a trajectory diagram in the system when the SNR is -13.8dB.

[0050] Figure 14 This invention provides a space-based single-photon lidar method and system for space debris ranging, and a trajectory diagram when the SNR is -15.9dB.

[0051] Figure 15 This invention provides a space-based single-photon lidar method for ranging space debris and a trajectory diagram in the system when the SNR is -20.0dB.

[0052] Figure 16 This is a system architecture diagram of a space-based single-photon lidar method and system for space debris ranging provided by the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0054] Currently, a commonly used algorithm is the signal extraction algorithm based on OC residual comparison. This algorithm divides the OC residual plane into a grid of equal size and compares the number of points in the grid with a set threshold to determine whether these points are signal points. However, the grid size and threshold in this algorithm are greatly affected by environmental conditions and the distance between space debris and the observation station. An automatic observation data processing algorithm based on Poisson filtering has also been implemented. These methods assume that noise points in the OC residual follow a Poisson distribution and that the signal photon echo residual falls on a straight line with an unknown slope within a short time interval. A tilted rectangular window is used to scan the data within a short time interval to identify local signal statistical characteristics, and the signal is extracted by determining the window boundaries where the data does not conform to the Poisson distribution. Furthermore, a deep convolutional neural network model has been used to effectively identify single-lens reflex echo images using the DeepLabcut tool. However, laser ranging data is one-dimensional. Since only a few textures or features can be seen in two-dimensional images or videos, it is difficult to apply current image processing models to laser ranging data.

[0055] Table 1:

[0056]

[0057] To compare existing algorithms with the sparse photon clustering algorithm of this application, the performance of the corresponding algorithms in Table 1 is summarized. The Poisson filtering algorithm can handle spatially sparse photon ranging data with relatively low computational complexity, but its accuracy and precision are limited, and it cannot handle nonlinear motion. DBSCAN is fast and can maintain the accuracy of the original data, but its efficiency fails if the density of noise photons is higher than the density of signal photons. Another drawback of DBSCAN is its long computation time, making it unable to track moving targets. The Hough transform algorithm provides satisfactory performance in the case of sparse signals. By rasterizing the original data, its accuracy (denoted as Δ) can be adjusted by changing the pixel resolution. However, its spatial complexity is not conducive to scaling accuracy. For targets with quadratic curve trajectories, the spatial complexity of the Hough transform can be expressed as (L / Δ). 3 , where L represents the range of each dimension of the parameter space of the original data. The sparse photon clustering algorithm of this application can potentially solve the above problems, providing excellent processing speed and high accuracy for sparse photon ranging data.

[0058] Reference Figure 1 As shown, in a first aspect, the present invention provides a space-based single-photon lidar method for space debris ranging, the method comprising:

[0059] S100, obtain the first velocity and first acceleration of the first photon;

[0060] In step S100, the steps of obtaining the first velocity and the first acceleration of the first photon include: obtaining the first data of the first photon; the first photon data includes the first time and the first distance; and calculating the first velocity and the first acceleration of the first photon in the time neighborhood based on the first time and the first distance.

[0061] In practical applications, obtaining the first time and first distance of the first photon from the entire domain, that is, at t i The distance at which the j-th photon is collected at time t is denoted as (t_j) i x ij If the first photon can be represented as D = {t}, then the first photon can be denoted as D = {t}. i x ij The velocity v of the first photon is calculated based on the first time and first distance obtained. ij Then, the acceleration 'a' of the first photon is calculated based on the velocity change and time interval of the first photon. ij .

[0062] S200, obtain the second velocity and second acceleration of the second photon;

[0063] In step S200, the step of obtaining the second velocity and the second acceleration of the second photon further includes: obtaining the second photon data of the second photon; the second photon data includes a second time and a second distance; and calculating the second velocity and the second acceleration of the second photon in the time neighborhood based on the second time and the second distance.

[0064] In practical applications, photon data of all photons other than the first photon are acquired from the entire domain. These other photons are referred to as the second photon, and this photon data is called the second data. Since the second data includes the time and distance of the second photon, the time is the second time of the second photon, and the distance is the second distance of the second photon. That is, it will be t i′ The distance at which the j-th photon is collected at time t is denoted as (t_j) i′ x i′j′ Then, based on the obtained second time and second distance, the velocity v of the second photon is calculated. i′j′ The acceleration 'a' of the second photon is then calculated based on its second velocity and second time. i′j′ The average time interval between the detection of the first and second photons is defined as the time neighborhood ∈, which is related to the detection efficiency of the reflected signal photon. This application identifies whether the current photon is a signal photon by calculating and statistically analyzing the velocity and acceleration of the photon over a period of time, where the width of the time neighborhood T = ∈.

[0065] S300, calculate the speed difference based on the first speed and the second speed;

[0066] In step S300, since the difference in velocity and acceleration between different photons is compared with a first preset threshold and a second preset threshold to determine whether a photon is a signal photon or a noise photon, it is necessary to first calculate the velocity difference between the first velocity and the second velocity. The absolute value of this velocity difference is expressed as...

[0067] S400, calculate the acceleration difference based on the first acceleration and the second acceleration;

[0068] In step S400, since the difference in velocity and acceleration between different photons is compared with a first preset threshold and a second preset threshold to determine whether a photon is a signal photon or a noise photon, it is necessary to first calculate the acceleration difference between the first acceleration and the second acceleration. The absolute value of this acceleration difference is expressed as...

[0069] S500: Based on the sparse photon clustering algorithm, the velocity difference, and the acceleration difference, cluster the photons across the entire domain to generate the target trajectory.

[0070] In step S500, the step of generating a target trajectory by clustering global photons according to the sparse photon clustering algorithm, the velocity difference, and the acceleration difference further includes: if the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then the acceleration difference or the photon corresponding to the velocity difference is added to the set; the photons in the set that are greater than the noise threshold and have the same velocity and acceleration are clustered to generate the target trajectory. After the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the step further includes: if the set is empty, then the search depth is doubled. After the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the step further includes: if the set is empty and the temporal neighborhood width is equal to the pulse period, then the photon search is terminated.

[0071] In practical applications, the first threshold is δ. v The second threshold is δ a If the speed difference is less than the first preset threshold Or the acceleration difference is less than the second preset threshold. Then let the first photon D = {(t i x ij Add the photons to set Ω, and determine if set Ω is empty. If set Ω is empty, double the search depth and search for other photons in the neighborhood again, i.e., the second photon. If T = Td, where T is the temporal neighborhood width and Td is the pulse period, and the set is empty, stop searching for photons in the neighborhood. If the set is not empty, vote on the velocity and acceleration of the photons in the set, and cluster photons that are greater than the noise threshold and have the same velocity or acceleration into {C}. K}, {C K The photons in} are signal photons, used in C K The photons in the data are fitted to generate the target trajectory y. K .

[0072] To verify the feasibility of the sparse photon clustering algorithm in this application, an SPL ground-based detection simulator is used as an example. The simulator includes a 17-meter-long collimator to simulate long-distance light field transmission, enabling high-precision pulse cutting and modulation of a continuous-wave (CW) laser using an acousto-optic modulator (AOM). An adjustable attenuator controls the light intensity. An arbitrary waveform generator (AWG) generates a sequence signal representing a specific frequency of the laser pulse, which is synchronized with the photon detection signal via a TCSPC. An FPGA programmable control module is used to control the width, delay, and intensity of the echo laser pulse, simulating echo photon data under different laser pulse widths, target distances, and reflectivities in a long-range SPL ranging system.

[0073] Photon data was obtained from the SPL ground-based detection facility, reference Figure 2 As shown, Figure 2 The original photon ranging data is displayed, where the signal photon is overwhelmed by noise photons. By calculating the velocity and acceleration of the photon in the time neighborhood, we obtain the acceleration value of the signal photon, referring to... Figure 3 As shown, this value is significantly higher than the acceleration of noise photons, referring to... Figure 4 As shown, photons with the correct acceleration are marked as signal photons, referring to... Figure 5 As shown, the target trajectory can be obtained by fitting these signal photon data points.

[0074] This application simulates and detects photons of targets moving along different trajectories using an SPL simulator, referencing... Figure 6 As shown, Figure 6 The results of target detection and trajectory extraction for a straight line trajectory are displayed when the computation time is 0.7ms. (Refer to...) Figure 7 As shown, Figure 7 The display shows the target detection and trajectory extraction results of the quadratic curve with a computation time of 5.6 ms. (Refer to...) Figure 8 As shown, Figure 8 The results of target detection and trajectory extraction for a cubic curve with a computation time of 7.4 ms are displayed. As the computational load required to solve for the correlation values ​​of velocity and acceleration increases, the required computation time also increases with the order of the curve. (Refer to...) Figure 9 As shown, Figure 9 The data and related results are shown when photon data of two targets with different trajectories are measured simultaneously with a computation time of 17.1 ms. Figure 10 The results show photon data and related findings when three targets with different trajectories are measured simultaneously with a computation time of 36.8 ms. As can be seen from the results, the algorithm of this application has good recognition and trajectory extraction capabilities for signal photons from targets with different trajectories and multiple targets under high background noise.

[0075] Reference Figures 11 to 15 As shown, the raw photon data and extraction results of a target with a quadratic trajectory are displayed under different signal and noise photon count rates. As the signal photon count rate (Ps) decreases, Figure 11 With Ps = 0.1 and SNR = -9.5dB, the calculation time is 9.0ms, where SNR is the signal-to-noise ratio. Figure 12 With Ps = 0.06, SNR = -12.9dB, and computation time of 7.6ms, Figure 13 With Ps = 0.04, SNR = -13.8dB, and computation time of 32.9ms, Figures 11 to 13 The photon count rate increases in the noise level. Figure 14With Ps = 0.05, SNR = -15.9dB, and computation time of 32.9ms, Figure 15 With Ps = 0.05, SNR = -20.0dB, and computation time of 67.0ms, Figures 14 to 15 A decrease in the mid-signal photon count rate leads to an increase in the search time neighborhood, resulting in a longer computation time. Conversely, a low SNR also contributes to the increased computation time because the search involves a large number of photons within the time neighborhood. Figure 15 As can be seen, even with a signal-to-noise ratio as low as -20dB, the algorithm in this application can still accurately locate the submerged signal photons.

[0076] The algorithm presented in this application can extract signal photons reflected by targets moving along arbitrary polynomial curve trajectories in high background noise. For targets with quadratic curve trajectories, under conditions of a signal-to-noise ratio of -20 dB and a signal photon count rate of 5%, the algorithm can achieve a ranging accuracy of over 99%. The computation time is on the order of tens of milliseconds, which is much faster than the traditional Hough transform algorithm. The algorithm has relatively simple time and space complexity and can be easily implemented in hardware processing systems. Our research provides an effective data processing method for single-photon lidar ranging of satellites or space debris.

[0077] Reference Figure 16 As shown, in a second aspect, this application provides a space-based single-photon lidar space debris ranging system, the system comprising: an acquisition module 100, a calculation module 200, and a clustering module 300;

[0078] The acquisition module 100 is configured to: acquire the first velocity and first acceleration of the first photon; acquire the second velocity and second acceleration of the second photon;

[0079] The calculation module 200 is configured to: calculate the speed difference based on the first speed and the second speed; and calculate the acceleration difference based on the first acceleration and the second acceleration.

[0080] The clustering module 300 is configured to: cluster global photons according to the sparse photon clustering algorithm, the velocity difference and the acceleration difference to generate the target trajectory.

[0081] In one implementation, the clustering module 300 is further configured as follows:

[0082] If the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then the acceleration difference or the photon corresponding to the velocity difference is added to the set;

[0083] Photons in the set that are greater than the noise threshold and have the same velocity and acceleration are clustered to generate the target trajectory.

[0084] The effects of applying the aforementioned method in the above system can be found in the description of the aforementioned method embodiments, and will not be repeated here.

[0085] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0086] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A space-based single-photon lidar method for space debris ranging, characterized in that, The method includes: Obtain the first velocity and first acceleration of the first photon; To obtain the second velocity and second acceleration of the second photon; Calculate the speed difference based on the first speed and the second speed; Calculate the acceleration difference based on the first acceleration and the second acceleration; The target trajectory is generated by clustering global photons based on the sparse photon clustering algorithm, the velocity difference, and the acceleration difference; The step of generating a target trajectory by clustering global photons according to the sparse photon clustering algorithm, the velocity difference, and the acceleration difference includes: if the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then adding the acceleration difference or the photon corresponding to the velocity difference to the set; and clustering the photons in the set that are greater than the noise threshold and have the same velocity and acceleration to generate the target trajectory. After the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method further includes: if the set is an empty set, then doubling the search depth; Alternatively, after the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method may further include: if the set is empty and the temporal neighborhood width is equal to the pulse period, then the photon search is terminated.

2. The space debris ranging method for a space-based single-photon lidar according to claim 1, characterized in that, The steps to obtain the first velocity and first acceleration of the first photon include: Acquire the first data of the first photon; the first photon data includes the first time and the first distance; Calculate the first velocity and first acceleration of the first photon in the time neighborhood based on the first time and the first distance.

3. The space debris ranging method of a space-based single-photon lidar according to claim 1, characterized in that, The steps for obtaining the second velocity and second acceleration of the second photon also include: Acquire the second photon data of the second photon; the second photon data includes a second time and a second distance; The second velocity and second acceleration of the second photon within the time neighborhood are calculated based on the second time and the second distance.

4. A space-based single-photon lidar space debris ranging system, characterized in that, The system includes: an acquisition module, a calculation module, and a clustering module; The acquisition module is configured to: acquire the first velocity and first acceleration of the first photon; acquire the second velocity and second acceleration of the second photon; The calculation module is configured to: calculate the speed difference based on the first speed and the second speed; and calculate the acceleration difference based on the first acceleration and the second acceleration. The clustering module is configured to cluster global photons according to the sparse photon clustering algorithm, the velocity difference, and the acceleration difference to generate the target trajectory; The step of generating a target trajectory by clustering global photons according to the sparse photon clustering algorithm, the velocity difference, and the acceleration difference includes: if the velocity difference is less than a first preset threshold or the acceleration difference is less than a second preset threshold, then adding the acceleration difference or the photon corresponding to the velocity difference to the set; and clustering the photons in the set that are greater than the noise threshold and have the same velocity and acceleration to generate the target trajectory. After the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method further includes: if the set is an empty set, then doubling the search depth; Alternatively, after the step of adding the acceleration difference or the photon corresponding to the velocity difference to the set, the method may further include: if the set is empty and the temporal neighborhood width is equal to the pulse period, then the photon search is terminated.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the space debris ranging method of a space-based single-photon lidar as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of a space-based single-photon lidar space debris ranging method as described in any one of claims 1 to 3.

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