A method and system for locating and tracking moving targets using single photon detection

Abstract

The application provides a single-photon detection moving target positioning and tracking method and system, and relates to the field of laser radar target positioning and tracking.The method realizes data pixel normalization, longitudinal velocity compensation of signals, accurate acquisition of pixel signal intensity and accurate inverse solution of a moving track through signal preprocessing, longitudinal motion compensation, pixel information extraction and transverse information acquisition, and finally obtains an accurate three-dimensional moving track of a target.The application solves the problem of three-dimensional positioning of a moving target based on a simple mechanical structure, and realizes three-dimensional positioning and tracking of the target.

Description

Technical Field

[0001] This invention belongs to the field of lidar target positioning and tracking technology, and particularly relates to a method and system for positioning and tracking moving targets using single-photon detection. Background Technology

[0002] Single-photon detection achieves single-photon-level sensitivity using a single-photon detector as the detection end. However, a single detection can only determine the presence or absence of a signal, not its strength. Often, a histogram is obtained by accumulating data over time to determine the target distance. Therefore, single-photon detection is widely used for long-range target detection. How to utilize single-photon detection technology to achieve three-dimensional spatial localization and tracking of moving targets has always been a source of great curiosity.

[0003] Long-range moving "small" targets mostly refer to distant aerial or space targets, whose signals can often only be measured within a single pixel. Therefore, the focus is primarily on target detection and ranging. However, distance only includes longitudinal information; acquiring lateral information and 3D localization are deeper application requirements for moving targets. Currently used photonic positioning technologies can be broadly categorized into single-point scanning and area array detection. For long-range moving targets, single-point scanning is more commonly used. Single-point scanning consists of a small field of view and a mechanical scanning structure, achieving target tracking through the design of the scanning method. However, the mechanical structure directly affects positioning accuracy and significantly increases the complexity of the lidar system. Therefore, how to achieve moving target localization and tracking using a photonic detection system based on a simple mechanical structure has become a problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for locating and tracking moving targets using single-photon detection.

[0005] The first aspect of this invention discloses a method for locating and tracking a moving target using single-photon detection; the method includes:

[0006] Step S1: Based on the fixed time delay of each pixel, normalize the echo photon events of the four pixels; that is:

[0007] r′ k =r″ k (t k →t′0)

[0008] In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized.

[0009] Step S2: Estimate or reduce the distance gate to perform preliminary denoising on the normalized photon event data;

[0010] Step S3: Based on the photon events of each pixel after preliminary denoising, use the random sampling consensus algorithm to filter signal points and fit the longitudinal motion trajectory;

[0011] Step S4: Perform motion compensation on the photon event data using the longitudinal motion trajectory;

[0012] Step S5: Using the compensated photon events, extract the echo signal intensity of each pixel;

[0013] Step S6: Combine echo signal strength and SG filtering to achieve smoothing of the signal curve;

[0014] Step S7: Use the intensity of the smoothed echo signal to obtain the tilt detection error between each pixel;

[0015] Step S8: Based on the tilt detection error and the theoretical error, solve for the position of the target spot on the focal plane;

[0016] Step S9: Determine whether to end the detection cycle. If not, proceed to the next detection time and repeat steps S1-S8. If it ends, continue to determine whether to end tracking. If not, when the target is about to leave the detection field of view, change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

[0017] Furthermore, in step S2, the estimated or narrowed fine distance gate is set to T. g , with T g / 3 represents the histogram grid width, and the step size for the cyclic statistics is T. g / 10, perform histogram statistics to achieve preliminary noise reduction, that is:

[0018] r k =r′ k (t i ∈T g )

[0019] In the formula, r k For each pixel after signal preprocessing; t i The detection time for the signal photon event.

[0020] Statistical records show the initial filtered noise M n1-k ;Right now

[0021] M n1-k =counts(r k (t i -k))

[0022] In the formula, counts() is a statistical counting function.

[0023] Furthermore, step S3 includes:

[0024] The signal data from the four pixels are fused to obtain the fused signal event S; that is...

[0025] S={r k k = 1, 2, 3, 4

[0026] The random sample consensus algorithm is used to process the signal event S and filter out the interior points S. inlier And by using the least squares method, the real-time longitudinal trajectory of the moving target can be obtained.

[0027] Further, step S4 specifically includes:

[0028] Compensated photon event r vc-k for:

[0029]

[0030] In the formula, The longitudinal distance compensated for by the signal photon event. f v The equation for the longitudinal motion distance of the target is t. i t0 represents the detection time of the signal photon event, and t0 represents the start time of the detection.

[0031] Furthermore, step S5 includes:

[0032] Let the longitudinal distance value R0 be equal to the longitudinal distance traveled at time t0, i.e., R0 = f v (t0);

[0033] Using the longitudinal distance value R0 as the center position of the signal, and based on the transmitted pulse width and the estimated size of the target being detected, a distance threshold R is set. thre The number of events M of the signal is obtained respectively. s-k The number of events M with noise n2-k :

[0034] M s-k =counts(r vc-k ∈[R0-R thre ,R0+R thre ])

[0035] M n2-k =counts(r vc-k ∈[0,R0-R thre ))

[0036] N per launchpulse The signal intensity N' can be obtained by extracting the signal intensity from the laser pulse and using the detected photon events. k 'for:

[0037]

[0038] In the formula, t tg It is the time grid corresponding to the target, t d It is the number of time grids corresponding to the dead time of a single-photon detector.

[0039] Further, in step S6, the smoothed signal strength N' k Represented as:

[0040] N′ k =sg(N″ k / η k (a,b)

[0041] In the formula, sg() is the SG filtering function, a is the order of the polynomial, b is the smoothed frame length, and η k This represents the detection efficiency of each pixel in a single-photon detector.

[0042] Furthermore, in step S7, the tilt detection error of the target in the four quadrants is:

[0043]

[0044]

[0045] In the formula, σ′ x Let σ′ be the x-axis tilt detection error. y This represents the y-axis tilt detection error.

[0046] Theoretical error σ x and σ y From the following formula, we get:

[0047]

[0048]

[0049] In the formula, N1, N2, N3, and N4 are the laser echo signal intensities detected by each pixel, respectively.

[0050] In step S8, combined with σ x and σ y Using interpolation search The minimum value is obtained, and thus the horizontal position of the target (x′0, y′0) is obtained.

[0051] A second aspect of this invention discloses a single-photon detection system for locating and tracking moving targets, the system comprising:

[0052] The first processing module is configured to normalize the echo photon events of four pixels based on the fixed time delay of each pixel; that is:

[0053] r′ k =r″ k (t k →t′0)

[0054] In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized.

[0055] The second processing module is configured to estimate or reduce the distance gate and perform preliminary denoising on the normalized photon event data.

[0056] The third processing module is configured to filter signal points based on the photon events of each pixel after preliminary denoising, using a random sampling consistency algorithm, and fit the longitudinal motion trajectory.

[0057] The fourth processing module is configured to perform motion compensation on the photon event data using the longitudinal motion trajectory.

[0058] The fifth processing module is configured to extract the echo signal intensity of each pixel using the compensated photon events.

[0059] The sixth processing module is configured to combine the echo signal strength and SG filtering to achieve smoothing of the signal curve;

[0060] The seventh processing module is configured to obtain the tilt detection error between each pixel by using the intensity of the smoothed echo signal.

[0061] The eighth processing module is configured to calculate the position of the target spot on the focal plane based on the tilt detection error and the theoretical error.

[0062] The ninth processing module is configured to determine whether to end the detection cycle. If not, it will step into the next detection time. If it ends, it will continue to determine whether to end the tracking. If not, when the target is about to leave the detection field of view, it will change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

[0063] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a single-photon detection moving target localization and tracking method according to any one of the first aspects of this disclosure.

[0064] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a single-photon detection moving target localization and tracking method according to any one of the first aspects of this disclosure.

[0065] In summary, the solution proposed in this invention has the following technical effects: Through signal preprocessing, longitudinal motion compensation, pixel information extraction, and lateral information acquisition, this invention achieves the goals of data pixel normalization, longitudinal velocity compensation of the signal, accurate acquisition of the signal intensity of each pixel, and precise inverse kinematics of the motion trajectory, ultimately obtaining an accurate three-dimensional motion trajectory of the target. This invention solves the problem of three-dimensional positioning of moving targets based on simple mechanical structures, realizing the three-dimensional positioning and tracking of the target. Attached Figure Description

[0066] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0067] Figure 1 This is a flowchart of a moving target localization and tracking method using single-photon detection according to an embodiment of the present invention;

[0068] Figure 2 This is a distribution diagram of photon events of a target in four quadrants according to an embodiment of the present invention;

[0069] Figure 3 This is a diagram showing the result of delay correction after data preprocessing according to an embodiment of the present invention;

[0070] Figure 4 This is a diagram showing the result after coarse denoising according to an embodiment of the present invention;

[0071] Figure 5 This is a longitudinal motion fitting curve according to an embodiment of the present invention;

[0072] Figure 6 To correct photon events according to embodiments of the present invention;

[0073] Figure 7This is a diagram showing the signal intensity results of each pixel extracted according to an embodiment of the present invention;

[0074] Figure 8 The image shows the result after smoothing the intensity according to an embodiment of the present invention;

[0075] Figure 9 This is a diagram showing the calculated tilt detection error in the four quadrants according to an embodiment of the present invention.

[0076] Figure 10 This is a two-dimensional mapping diagram according to an embodiment of the present invention;

[0077] Figure 11 The lateral movement trajectory of the target light spot according to an embodiment of the present invention;

[0078] Figure 12 The three-dimensional trajectory of the target according to an embodiment of the present invention is shown in the figure;

[0079] Figure 13 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of this application, a first image may be referred to as a second image, and similarly, a second image may be referred to as a first image. Both the first image and the second image are images, but they are not the same image.

[0082] The first aspect of this invention discloses a method for locating and tracking a moving target using single-photon detection; the method includes:

[0083] Step S1: Normalize the echo photon events of the four pixels based on the fixed delay of each pixel; since there are certain differences between the fixed delays of each pixel, we first normalize the four quadrants, that is:

[0084] r′ k =r″ k (t k→t′0)

[0085] In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized.

[0086] Step S2: Estimate or reduce the distance gate to perform preliminary denoising on the photon event data;

[0087] In step S2, if there is no distance gate or the distance gate is too large, to reduce the impact of noise and the computational load, we implement coarse denoising through distance gate selection. The fine distance gate after estimating or reducing the distance gate is set to T. g , with T g / 3 represents the histogram grid width, and the step size for the cyclic statistics is T. g / 10, perform histogram statistics, and select the distance gate with the largest histogram peak. This achieves initial noise reduction.

[0088] r k =r′ k (t i ∈T g )

[0089] In the formula, r k For each pixel after signal preprocessing; t i The detection time for the signal photon event;

[0090] Statistical records show the initial filtered noise M n1-k ;Right now

[0091] M n1-k =counts(r k (t i -k))

[0092] In the formula, counts() is a statistical counting function, and k = 1, 2, 3, 4.

[0093] Step S3: Use the random sampling consensus algorithm to filter signal points and fit the longitudinal motion trajectory;

[0094] Since each pixel detects the same target, their longitudinal trajectories are essentially identical. To reduce errors caused by the randomness of photon events, step S3 includes:

[0095] The signal data from the four pixels are fused to obtain the fused signal event S; that is...

[0096] S={r k k = 1, 2, 3, 4

[0097] The random sample consensus algorithm is used to process the signal event S and filter out the interior points S. inlier And by using the least squares method, the real-time longitudinal trajectory of the moving target can be obtained.

[0098]

[0099] In the formula, S inlier ={(R i ,t i ), i = 1,, Num s}, t i R is the detection time of the signal photon event. i The detection range for each signal photon event; f v The equation for the longitudinal motion distance of the target includes the longitudinal velocity of the target.

[0100] Step S4: Perform motion compensation on the photon event data using the longitudinal motion trajectory;

[0101] Step S4 specifically includes:

[0102] Compensated photon event r vc-k for:

[0103]

[0104] In the formula, The longitudinal distance compensated for by the signal photon event. f v The equation for the longitudinal motion distance of the target is t. i t0 represents the detection time of the signal photon event, and t0 represents the start time of the detection.

[0105] Step S5: Using the compensated photon events, extract the echo signal intensity of each pixel;

[0106] Step S5 includes:

[0107] Let the longitudinal distance value R0 be equal to the longitudinal distance traveled at time t0, i.e., R0 = f v (t0);

[0108] Using the longitudinal distance value R0 as the center position of the signal, based on the transmitted pulse width FWHM pulse Based on the estimated size of the target being detected, a distance threshold R is set. thre Assuming the signal exists only in this region, the number of events M of the signal is obtained respectively. s-k The number of events M with noise n2-k :

[0109] M s-k=counts(r vc-k ∈[R0-R thre ,R0+R thre ])

[0110] M n2-k =counts(r vc-k ∈[0,R0-R thre ))

[0111] N per launch pulse The signal intensity N″ can be obtained by extracting the signal intensity from the laser pulse and using the detected photon events. k for:

[0112]

[0113] In the formula, t tg It is the time grid corresponding to the target, t d It is the number of time grids corresponding to the dead time of a single-photon detector.

[0114] Step S6: Combine echo signal strength and SG filtering to achieve smoothing of the signal curve;

[0115] In step S6, the smoothed signal strength N' k Represented as:

[0116] N′ k =sg(N″ k / η k (a,b)

[0117] In the formula, sg() is the SG filtering function, a is the order of the polynomial, b is the smoothed frame length, and η k This represents the detection efficiency of each pixel in a single-photon detector.

[0118] Step S7: Use the intensity of the smoothed echo signal to obtain the tilt detection error between each pixel;

[0119] In step S7, the tilt detection error of the target in the four quadrants is:

[0120]

[0121]

[0122] Step S8: Based on the theoretical error, determine the position of the target spot on the focal plane;

[0123] In the formula, σ′ x Let σ′ be the x-axis tilt detection error. y This represents the y-axis tilt detection error.

[0124] Theoretical error σ x and σ y From the following formula, we get:

[0125]

[0126]

[0127] In the formula, N1, N2, N3, and N4 are the laser echo signal intensities detected by each pixel, respectively.

[0128] In step S8, combined with σ x and σ y Using interpolation search The minimum value is obtained to obtain the horizontal position of the target (that is, the position of the target spot on the focal plane) (x′0, y′0).

[0129] Step S9: Determine whether to end the detection cycle. If not, proceed to the next detection time and repeat steps S1-S8. If it ends, continue to determine whether to end tracking. If not, when the target is about to leave the detection field of view, change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

[0130] The method will now be described using a specific implementation method:

[0131] The photosensitive surface radius of the four-quadrant detector is set to w0 = 45 μm, and the detection probability and time delay of the four pixels are respectively... The dead time is 1 microsecond. The radius w of the light spot on the photosensitive surface is... t =50μm. The target's longitudinal velocity is 100m / s, and its lateral velocity is 300μm / s in the direction (1,1). The initial target spot center is (-20, -20)μm. The total number of echo photons reaching the detector surface is 20, and the laser repetition rate is 25kHz. The detection noise is 1MHz. The range gate is 400ns (corresponding to 60m). After Monte Carlo simulation, the photon event distribution of the target in the four quadrants can be obtained, as follows: Figure 2 As shown.

[0132] After 0.2 seconds of detection, the photon events of the four pixels showed differences, and the noise was still relatively high. After time delay correction in step S1 data preprocessing, the result is as follows: Figure 3 .

[0133] The fine distance gate size is set to 30ns, and the result after coarse denoising in step S2 is as follows. Figure 4 It can be seen that the processing effectively reduced noise and highlighted the signal.

[0134] The data from four pixels are fused, and RANSAC is used to filter signal points. A point is the photon event S that initializes the signal, and a rhombus is the interior point S to be filtered. inlier (i.e. signal point), f v For the longitudinal motion fitting curve, see Figure 5 It can be observed that the selected signal points are more concentrated, fitting the longitudinal motion trajectory of the target.

[0135] Using the longitudinal trajectory equation, motion compensation is performed on the entire photon event to correct the photon event as follows: Figure 6 As shown, the center distance value of the target at each time step is obtained after correction.

[0136] Using the compensated signal photon events, the signal intensity of each pixel is extracted, and the result is as follows: Figure 7 It can be observed that the recovered signal strength exhibits some fluctuations.

[0137] Smooth the intensity, such as Figure 8 As shown, the thin curve represents the ideal intensity value, while the curve marked with a triangle represents the smoothed intensity of each pixel. It can be observed that the recovered signal variation trend is basically consistent with the theoretical value, with the signal intensity ranging from 0.01 to 1.56 photons.

[0138] Using the calculated signal strength, the tilt detection error in the four quadrants is calculated, such as... Figure 9 As shown, the horizontal axis represents the detection time, the left vertical axis represents the error on the x-axis, and the right vertical axis represents the error on the y-axis.

[0139] First, we calculate the theoretical error, and its two-dimensional mapping diagram is as follows: Figure 10 As shown, the left figure represents the error amount corresponding to the x-axis, and the right figure represents the error amount corresponding to the y-axis.

[0140] Then, by solving, the lateral motion trajectory of the target light spot can be obtained, such as... Figure 11 As shown, xy represents the theoretical lateral trajectory of the target light spot, and x'-y' represents the final lateral trajectory of the target light spot obtained by the method proposed in this patent. It can be seen that the trajectory obtained by the inverse solution is basically the same as the theoretical trajectory.

[0141] By combining the longitudinal motion information of the target given in step 3, the three-dimensional positioning information of the target can be obtained.

[0142] In three-dimensional space, assuming the focal length of the receiving system is 60mm and the system distance from the front of the gate is set to 10km, the target's three-dimensional trajectory can be obtained as shown in the figure. Figure 12 As shown.

[0143] In summary, this invention can effectively determine the lateral and longitudinal motion trajectory and velocity of a target, thereby obtaining the three-dimensional positioning information of the moving target and ultimately achieving the positioning and tracking of distant targets.

[0144] A second aspect of this invention discloses a single-photon detection system for locating and tracking moving targets, the system comprising:

[0145] The first processing module is configured to normalize the echo photon events of four pixels based on the fixed time delay of each pixel; that is:

[0146] r′ k =r″ k (t k →t′0)

[0147] In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized.

[0148] The second processing module is configured to estimate or reduce the distance gate and perform preliminary denoising on the normalized photon event data.

[0149] The third processing module is configured to filter signal points based on the photon events of each pixel after preliminary denoising, using a random sampling consistency algorithm, and fit the longitudinal motion trajectory.

[0150] The fourth processing module is configured to perform motion compensation on the photon event data using the longitudinal motion trajectory.

[0151] The fifth processing module is configured to extract the echo signal intensity of each pixel using the compensated photon events.

[0152] The sixth processing module is configured to combine the echo signal strength and SG filtering to achieve smoothing of the signal curve;

[0153] The seventh processing module is configured to obtain the tilt detection error between each pixel by using the intensity of the smoothed echo signal.

[0154] The eighth processing module is configured to calculate the position of the target spot on the focal plane based on the tilt detection error and the theoretical error.

[0155] The ninth processing module is configured to determine whether to end the detection cycle. If not, it will step into the next detection time. If it ends, it will continue to determine whether to end the tracking. If not, when the target is about to leave the detection field of view, it will change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

[0156] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a single-photon detection moving target localization and tracking method according to any one of the first aspects of this disclosure.

[0157] Figure 13 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 13 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0158] Those skilled in the art will understand that Figure 13 The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0159] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a single-photon detection method for locating and tracking a moving target according to any one of the first aspects of this disclosure.

[0160] In summary, the technical solution proposed in this invention has the following technical effects:

[0161] This application effectively solves the problem of lateral information acquisition and three-dimensional positioning of long-distance moving "small" targets. Based on the four-quadrant tracking principle, combined with longitudinal motion compensation and intensity information recovery, it realizes the acquisition of the target's three-dimensional velocity and trajectory, enriches the target information, and effectively realizes the three-dimensional positioning of moving targets in a simple system.

[0162] The method proposed in this application can not only realize the three-dimensional positioning and tracking of moving targets under the single-photon detection system and obtain the longitudinal and lateral motion speed and trajectory of the target, but also greatly simplify the complexity of the photon detection system and reduce the high precision requirements of the mechanical scanning system.

[0163] The moving targets in this application are not limited by target structure and have a large dynamic range of motion speed, which effectively expands the application scenarios of single-photon detection and provides exciting news for applications such as positioning and tracking of extremely distant aerial targets and multi-dimensional information acquisition for space debris detection.

[0164] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for locating and tracking a moving target using single-photon detection, characterized in that, The method includes: Step S1: Based on the fixed time delay of each pixel, normalize the echo photon events of the four pixels; that is: r′ k =r″ k (t k →t′0) In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized. Step S2: Estimate or reduce the distance gate to perform preliminary denoising on the normalized photon event data; Step S3: Based on the photon events of each pixel after preliminary denoising, use the random sampling consensus algorithm to filter signal points and fit the longitudinal motion trajectory; Step S4: Perform motion compensation on the photon event data using the longitudinal motion trajectory; Step S5: Using the compensated photon events, extract the echo signal intensity of each pixel; Step S6: Combine echo signal strength and SG filtering to achieve smoothing of the signal curve; Step S7: Use the intensity of the smoothed echo signal to obtain the tilt detection error between each pixel; Step S8: Based on the tilt detection error and the theoretical error, solve for the position of the target spot on the focal plane; Step S9: Determine whether to end the detection cycle. If not, proceed to the next detection time and repeat steps S1-S8. If it ends, continue to determine whether to end tracking. If not, when the target is about to leave the detection field of view, change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

2. The method according to claim 1, characterized in that, In step S2, the estimated or reduced fine distance gate is set to T. g , with T g / 3 represents the histogram grid width, and the step size for the cyclic statistics is T. g / 10, perform histogram statistics to achieve preliminary noise reduction, that is: r k =r′ k (t i ∈T g ) In the formula, r k For each pixel after signal preprocessing; t i The detection time for the signal photon event; Statistical records show the initial filtered noise M n1-k ;Right now M n1-k =counts(r k (t i -k)) In the formula, counts() is a statistical counting function.

3. The method according to claim 2, characterized in that, Step S3 includes: The signal data from the four pixels are fused to obtain the fused signal event S; that is... S={r k ,k=1,2,3,4} The random sample consensus algorithm is used to process the signal event S and filter out the interior points S. inlier And by using the least squares method, the real-time longitudinal trajectory of the moving target can be obtained.

4. The method according to claim 3, characterized in that, Step S4 specifically includes: Compensated photon event r vc- k is: In the formula, The longitudinal distance compensated for by the signal photon event. f v Let t0 be the equation for the longitudinal movement distance of the target, and t0 be the start time of the detection.

5. The method according to claim 4, characterized in that, Step S5 includes: Let the longitudinal distance value R0 be equal to the longitudinal distance traveled at time t0, i.e., R0 = f v (t0); Using the longitudinal distance value R0 as the center position of the signal, and based on the transmitted pulse width and the estimated size of the target being detected, a distance threshold R is set. thre The number of events M of the signal is obtained respectively. s-k The number of events M with noise n2-k : M s-k =counts(r vc-k ∈[R0-R thre ,R0+R thre ]) M n2-k =counts(r vc-k ∈[0,R0-R thre )) N per launch pulse The signal intensity N″ can be obtained by extracting the signal intensity from the laser pulse and using the detected photon events. k for: In the formula, t tg It is the time grid corresponding to the target, t d It is the number of time grids corresponding to the dead time of a single-photon detector.

6. The method according to claim 5, characterized in that, In step S6, the smoothed signal strength N' k Represented as: N′ k =sg(N″ k / or k ,a,b) In the formula, sg() is the SG filtering function, a is the order of the polynomial, b is the smoothed frame length, and η k This represents the detection efficiency of each pixel in a single-photon detector.

7. The method according to claim 6, characterized in that, In step S7, the tilt detection error of the target in the four quadrants is: In the formula, σ′ x Let σ′ be the x-axis tilt detection error. y This represents the y-axis tilt detection error. Theoretical error σ x and σ y From the following formula, we get: In the formula, N1, N2, N3, and N4 are the laser echo signal intensities detected by each pixel, respectively. In step S8, combined with σ x and σ y Using interpolation search The minimum value is obtained, and thus the position (x′0, y′0) of the target spot on the focal plane is obtained.

8. A moving target positioning and tracking system using single-photon detection, characterized in that, The system includes: The first processing module is configured to normalize the echo photon events of four pixels based on the fixed time delay of each pixel; that is: r′ k =r″ k (t k →t′0) In the formula, r″ k For the raw photon events of k pixels recorded, r′ k For photon events after time-delay normalization, t k t'0 represents the time when k pixels are not normalized, and t'0 represents the time when each pixel needs to be normalized. The second processing module is configured to estimate or reduce the distance gate and perform preliminary denoising on the normalized photon event data. The third processing module is configured to filter signal points based on the photon events of each pixel after preliminary denoising, using a random sampling consistency algorithm, and fit the longitudinal motion trajectory. The fourth processing module is configured to perform motion compensation on the photon event data using the longitudinal motion trajectory. The fifth processing module is configured to extract the echo signal intensity of each pixel using the compensated photon events. The sixth processing module is configured to combine the echo signal strength and SG filtering to achieve smoothing of the signal curve; The seventh processing module is configured to obtain the tilt detection error between each pixel by using the intensity of the smoothed echo signal. The eighth processing module is configured to calculate the position of the target spot on the focal plane based on the tilt detection error and the theoretical error. The ninth processing module is configured to determine whether to end the detection cycle. If not, it will step into the next detection time. If it ends, it will continue to determine whether to end the tracking. If not, when the target is about to leave the detection field of view, it will change the range gate and the center position of the field of view, and cycle through the next frame rate tracking cycle until the tracking finally ends.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the single-photon detection moving target positioning and tracking method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a single-photon detection method for locating and tracking moving targets according to any one of claims 1 to 7.

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