Post-target range multi-target trajectory measurement methods, systems, storage media, and processors

Abstract

This application applies to the field of target measurement technology at firing ranges, providing a method, system, storage medium, and processor for post-mission multi-target trajectory measurement at firing ranges. This method departs from the traditional "separate first, then rendezvous" approach, instead employing a "rendezvous first, then separate" process. First, it utilizes the azimuth and elevation angles of the target relative to multiple stations to perform combined rendezvous calculations for targets at various times, obtaining the three-dimensional position coordinates of multiple targets at each time point. This achieves dimensionality enhancement of the data to be separated. By introducing additional one-dimensional data, the overlap between target data is significantly reduced, lowering the difficulty of trajectory separation. Simultaneously, an unsupervised learning method—a clustering algorithm—is introduced. This algorithm can group data without any guidance and is highly efficient. The post-mission multi-target trajectory measurement system at firing ranges using this method also achieves the aforementioned effects.

Description

Technical Field

[0001] This application belongs to the field of target measurement technology at a test range, and particularly relates to a method, system, storage medium, and processor for post-test range multi-target trajectory measurement. Background Technology

[0002] Currently, an optoelectronic theodolite is an optical measuring device capable of automatically tracking the trajectory of a target and recording the angle change and image information of the target relative to a measurement reference. It uses single-lever control or data guidance in both azimuth and elevation directions to keep the target within the field of view and to center the image as much as possible, while simultaneously storing and recording the image for trajectory calculation.

[0003] Existing methods for measuring target trajectories using photoelectric theodolites mainly involve multi-station intersection positioning, as illustrated in the attached diagram. Figure 1 As shown, multiple photoelectric theodolites are used to simultaneously observe the target. Then, the image data from each theodolite, the recorded azimuth and elevation angle data, and the station locations are used to perform intersection calculations to obtain the target's position coordinates at each time, i.e., the target's trajectory. However, for multi-target measurements, each image contains multiple targets. For images from different stations at the same time, it is impossible to determine which targets are the same. Similarly, for images from the same station at different times, it is also impossible to determine which targets are the same. Therefore, unlike single-target measurements, the target trajectory cannot be directly obtained by performing intersection calculations on data from the same time, which brings difficulties to multi-target trajectory measurements.

[0004] The measurement system uses an electro-optical theodolite to track and calculate the azimuth and elevation angles of the target relative to the station from the measured images. First, it needs to identify the same target at different times at a single station, and then perform combined intersection positioning. However, azimuth and elevation angles are two-dimensional data, and there are many data intersection points between different targets, resulting in a high misjudgment rate during separation.

[0005] Meanwhile, the separation method uses a method of traversing all target data for fitting, and several sets of guide points must be given before fitting. The separation process is inefficient and lacks automation. Existing technologies have shortcomings. Summary of the Invention

[0006] The purpose of this application is to provide a method, system, storage medium, and processor for post-target range multi-target trajectory measurement, which aims to solve the technical problem that in the prior art, when using photoelectric theodolites to measure target trajectories, it is impossible to determine which targets are the same.

[0007] On the one hand, this application provides a method for post-target range multi-target trajectory measurement, the method comprising the following steps:

[0008] s1. Target tracking: Organize multiple photoelectric theodolites at different stations to work together to track and measure multiple targets, acquire target images, and form a sequence;

[0009] s2. Multi-target miss distance extraction: Based on the pixel position of the target in the image, calculate the pixel position of its deviation from the image center, which is used for subsequent calculation of the target's azimuth and elevation angles;

[0010] s3. Target azimuth and elevation angle calculation: Calculate the target's direction relative to the photoelectric theodolite based on the miss distance, detector intrinsic parameters, and theodolite encoder values;

[0011] s4. Combined intersection positioning: Perform intersection positioning calculations and filtering on multiple target points from different stations at the same time to obtain the position coordinates of all targets at the current time;

[0012] s5. Multi-target trajectory recognition: After completing the combined intersection and localization calculation in all images, the clustering algorithm is used to complete the trajectory recognition of the same target at different times.

[0013] On the other hand, this application also provides a post-test range multi-target trajectory measurement system, which adopts the post-test range multi-target trajectory measurement method as described above. The system includes multiple photoelectric theodolites at different stations working in concert. The photoelectric theodolites include: a tracking frame, an optical system, an image detector, an image processing and display system, an image storage system, a servo control system, and a data communication system.

[0014] On the other hand, this application also provides a storage medium storing program files capable of implementing the above-described post-target range multi-target trajectory measurement method.

[0015] On the other hand, this application also provides a processor for running a program, wherein the program executes the above-described post-target range multi-target trajectory measurement method during runtime.

[0016] This application's post-test range multi-target trajectory measurement method changes the "separate then rendezvous" approach, instead employing a "rendezvous then separation" process. First, it uses the target's different azimuth and elevation angles relative to multiple stations to perform combined rendezvous calculations for targets at various times. Then, it uses the two-dimensional azimuth and elevation angle data to calculate the three-dimensional target position information. Finally, it uses the shortest distance between the principal optical axes of different stations observing the same target at the same time to separate different targets. This yields the three-dimensional position coordinates of multiple targets at each time point, achieving dimensionality enhancement of the data to be separated. By introducing additional one-dimensional data, the overlap between target data is significantly reduced, lowering the difficulty of trajectory separation. Simultaneously, it introduces an unsupervised learning method—a clustering algorithm—which can group data without any guidance and is highly efficient. The post-test range multi-target trajectory measurement system using this method also exhibits the above effects. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of single-target multi-station rendezvous and positioning, which is referenced in the post-test range multi-target trajectory measurement method of this application;

[0018] Figure 2 This is a diagram showing the architecture of the post-launch multi-target trajectory measurement system for the test range in this application;

[0019] Figure 3 This is a detailed flowchart of the post-test range multi-target trajectory measurement method of this application;

[0020] Figure 4 This is a schematic diagram illustrating the target tracking measurement principle of the post-test range multi-target trajectory measurement method of this application;

[0021] Figure 5 This is a schematic diagram illustrating the principle of target miss distance extraction in the post-test range multi-target trajectory measurement method of this application;

[0022] Figure 6 This is a schematic diagram illustrating the principle of the intersection and localization algorithm in the post-target range multi-target trajectory measurement method of this application;

[0023] Figure 7 This is a diagram showing the results of multi-target combination intersection and positioning during the application of the post-test range multi-target trajectory measurement method of this application;

[0024] Figure 8 This is a diagram showing the trajectory recognition results of the spectral clustering method during the application of the post-application multi-target trajectory measurement method in this application;

[0025] Figure 9 This is a flowchart illustrating the implementation of the post-test range multi-target trajectory measurement method of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] The specific implementation of this application will be described in detail below with reference to specific embodiments:

[0028] Example 1:

[0029] Figure 9 The implementation flow of the post-target range multi-target trajectory measurement method provided in Embodiment 1 of this application is illustrated. For ease of explanation, only the parts related to the embodiments of this application are shown, and are described in detail below:

[0030] On the one hand, this application provides a method for post-target range multi-target trajectory measurement, the method comprising the following steps:

[0031] s1. Target tracking: Organize multiple photoelectric theodolites at different stations to work together to track and measure multiple targets, acquire target images, and form a sequence;

[0032] s2. Multi-target miss distance extraction: Based on the pixel position of the target in the image, calculate the pixel position of its deviation from the image center, which is used for subsequent calculation of the target's azimuth and elevation angles;

[0033] s3. Target azimuth and elevation angle calculation: Calculate the target's direction relative to the photoelectric theodolite based on the miss distance, detector intrinsic parameters, and theodolite encoder values;

[0034] s4. Combined intersection positioning: Perform intersection positioning calculations and filtering on multiple target points from different stations at the same time to obtain the position coordinates of all targets at the current time;

[0035] s5. Multi-target trajectory recognition: After completing the combined intersection and localization calculation in all images, the clustering algorithm is used to complete the trajectory recognition of the same target at different times.

[0036] The process of applying the above method to a measurement system can be found in the appendix. Figure 3 As shown. This application uses data from the same time point for intersection and eliminates false targets by using the straight-line distance between opposite planes. Then, it uses a clustering algorithm to complete trajectory recognition. By using three-dimensional data information for trajectory recognition, it reduces the problem of many overlapping data generated when using two-dimensional data of azimuth and elevation angles for recognition, which leads to difficulties in data separation.

[0037] As attached Figure 4 As shown, step s1 for target tracking includes the following steps:

[0038] s11. Before tracking begins, the photoelectric theodolite is positioned according to the actual observation and leveling is completed.

[0039] s12. During the tracking process, the servo control system controls the rotation of the tracking frame of the photoelectric theodolite. The target optical signal reaches the image detector through the optical system and is converted into a digital signal that is easy to process. The signal is then sent to the image processing and display system for real-time display by the data communication system. The operator makes real-time adjustments to the servo control based on the displayed image to achieve stable tracking of the target.

[0040] s13. The data communication system also sends the digital signal converted by the image detector to the image storage system for storage, so as to be used for subsequent trajectory separation.

[0041] Furthermore, step s1 also includes: during the target tracking process, the photoelectric theodolite uses the encoder subsystem to record the azimuth and pitch angle of the photoelectric theodolite itself in real time.

[0042] Further details are attached. Figure 5 As shown, the relationship between the target azimuth and elevation angles and the miss distance is defined as follows: Under ideal conditions, the target is located at the center of the image plane during tracking. At this time, the angle of rotation of the theodolite is the azimuth and elevation angles of the target relative to the theodolite. However, due to the irregularity of the target's movement and the lag in operation, the target often deviates from the center of the image plane, which means that the real-time recorded theodolite rotation angle cannot be directly used as the target azimuth and elevation angles. It is necessary to correct for the target's deviation from the center on the image based on the distances ∆X and ∆Y, which is the miss distance.

[0043] Furthermore, step s2 includes:

[0044] s21. Preprocessing of the image including non-linear stretching, denoising, sharpening, and enhancement;

[0045] s22. The Ostu thresholding method is used to calculate the image threshold, and the image threshold is used to segment the original image to complete the separation of the target and the background;

[0046] s23. Extract the pixel position of the target, and calculate the pixel position of the target that is offset from the center of the image based on the pixel position of the target in the image.

[0047] Specifically, the key to off-target measurement is obtaining the centroid position of the target. During target tracking with an electro-optical theodolite, due to significant noise interference, the image must first be preprocessed using appropriate methods, including nonlinear stretching, denoising, sharpening, and enhancement. Then, a threshold-based segmentation method is used to extract the target. This application preferably uses the Ostu threshold segmentation method to calculate the image threshold, which is then used to segment the original image, separating the target from the background and extracting the pixel position of the target. The threshold segmentation formula is as follows:

[0048] ;

[0049] Pixel coordinates after image segmentation grayscale value at that location Original image pixel coordinates The grayscale value at that location; The threshold value is calculated using the Ostu threshold segmentation method. Values ​​greater than the threshold value are considered threshold values. The pixel position is the target position. The pixel coordinates of the segmented image are the same as those of the original image.

[0050] Finally, the centroid position is calculated based on the target pixel information. The centroid calculation formula is as follows:

[0051] ;

[0052] ;

[0053] Where U is the total number of columns in the image and V is the total number of rows in the image data.

[0054] Take the pixel coordinates of the image center position as Then the off-target amount The calculation formula is as follows:

[0055] .

[0056] Furthermore, step s3 includes:

[0057] s31. Based on the focal length and pixel size of the optical system in the photoelectric theodolite, calculate the angle corresponding to one pixel interval in the image when the optical center of the optical system is the vertex;

[0058] s32. Based on the angle corresponding to the pixel interval and the miss distance, the angle of the target deviating from the optical center is obtained, thereby completing the correction of the azimuth pitch angle.

[0059] Specifically, after the target miss distance is extracted in step s2, the real-time recorded theodolite rotation angle can be corrected to obtain the true angle of the target relative to the theodolite.

[0060] The angle corresponding to one pixel interval in the image, with the optical center of the optical system as the vertex, can be calculated from the focal length and pixel size of the theodolite's optical system. as follows:

[0061] ;

[0062] in, For the focal length of the optical system, This refers to the pixel size.

[0063] Combining the above formula with the miss distance, we can further obtain the angle by which the target deviates from the optical center, thus completing the correction of the azimuth elevation angle. Assume the azimuth angle of the photoelectric theodolite is... The pitch angle is The formula for calculating the final target's azimuth and elevation values ​​relative to the photoelectric theodolite is as follows:

[0064] Where A is the azimuth angle of the target relative to the photoelectric theodolite, and E is the elevation angle of the target relative to the photoelectric theodolite.

[0065] Further details are attached. Figure 6As shown, in step s4 of the combined rendezvous and positioning, the rendezvous and positioning calculation is performed using the non-plane rendezvous method; and false targets are eliminated by using the non-plane ray distance and rendezvous accuracy to obtain the position coordinates of each target at each moment.

[0066] Appendix Figure 6 middle For the location of the measuring station, line segment Two skew rays and The common perpendicular, even if the skew rays are obtained and The target location obtained by intersection positioning is the point M on the line segment connecting the closest points in space. For the purpose of measuring station Starting from the azimuth and elevation angles, the direction is the azimuth and elevation angle. ray.

[0067] Specifically, the intersection positioning adopts the non-plane intersection method, in the attached... Figure 6 In the O-xyz spatial rectangular coordinate system, a photoelectric theodolite is known. coordinates ( The azimuth angle obtained by measuring target M is... Pitch angle is The spatial straight lines formed are Theoretically speaking, rays and They can intersect at point M, but due to the influence of the photoelectric theodolite's structure and imaging mechanism, angle measurement accuracy, time synchronization, differences in the tracking target location, and the working environment, they cannot be aligned. Figure 6 In and They cannot intersect, meaning that when two photoelectric theodolites observe the same target, their principal optical axes do not intersect, and they appear to be in a non-intersecting relationship.

[0068] At this point, based on the coordinates of the two photoelectric theodolites and the measured azimuth and elevation angles, the following can be derived: The equation of the line segment, and ⊥ , among which, line segment For rays Part of; available The coordinate values. Then, on its common perpendicular line... A point was selected based on the angular measurement accuracy of the two theodolites. This serves as an estimate of the target's true location. The selection is made using the weighting coefficients in the formula below. In control, theodolites with high angular measurement accuracy are assigned higher weights. For example, the last three equations in the following intersection formula have weighting coefficients. Adjust through this coefficientM The location of the point, for example, the first station has higher accuracy, then The allocation is larger, corresponding to the coefficient of the second station (1- It's smaller.

[0069] Coordinates are calculated using the intersection of non-plane methods: ;

[0070] In the above formula: The weighting coefficients, appropriately selected based on the varying angular measurement accuracies of different theodolites, represent the distance between skew rays. for length, For line segments With line segment The angle between the two points. This application adopts a method of intersection followed by separation to achieve dimensionality increase and reduce intersection points, thereby reducing the false positive rate, and applies a clustering algorithm to ballistic separation instead of fitting to improve separation efficiency.

[0071] When measuring multiple targets, each station has multiple sets of azimuth and elevation values ​​at the same time. It is impossible to determine which data belong to the same target, so it is necessary to combine the azimuth and elevation data at the same time with the station locations for cross-intersection. During the cross-intersection process, many false targets will be generated. This application uses the eccentric ray distance d to eliminate these false targets.

[0072] The key point of the above embodiments of this application is to combine and rendezvous the flight data of multiple flying targets at the same time, and to use the ray distance of different planes to eliminate false targets; then, to use a clustering algorithm to complete trajectory recognition, and to use three-dimensional data information to perform trajectory recognition, thereby reducing the difficulty of data separation caused by the large overlap of two-dimensional data such as azimuth and pitch angles in the past.

[0073] Specifically, according to the principle of the non-linear intersection positioning method, the distance between non-linear rays is minimized when two theodolites observe the same target point at the same time. However, when observing different targets (i.e., using the azimuth and elevation angles of different targets at the same time for intersection calculation), due to the mismatch of angle information, even in the ideal state without any errors, the rays formed by the two stations and their respective azimuth and elevation angles will not intersect at a single point, and the distance d between non-linear rays will be very large. Therefore, intersection calculations can be performed by arranging and combining the azimuth and elevation angle information of different targets at the same time. The combination with the smallest non-linear ray distance corresponds to the data of the same target at different stations at the same time, and the position coordinates obtained by intersecting other target data are false targets.

[0074] The logic of the combination is to perform intersection calculations on the azimuth and elevation angles of target 1 at station 1 and the azimuth and elevation angles of all targets at station 2, and find the set of data with the smallest straight-line distance between the opposite planes. This set of data is the azimuth and elevation angle of the same target (target 1) observed by station 2 at this moment. Other targets are also calculated by finding matching data from the two stations in the same way.

[0075] Furthermore, in step s5, based on the fact that there are no strong abrupt changes during the movement of the same target, the movement trajectory is relatively smooth and has a certain regularity; and there is a certain distance between different targets; the DBSCAN clustering algorithm is used to divide a set of unlabeled datasets into several groups or clusters, and based on unsupervised learning, the data points within the same cluster have high similarity to each other, while the data points in different clusters have low similarity; thus, target trajectory recognition is achieved.

[0076] Specifically, after obtaining the coordinates of each target position at each moment, the following can be obtained: Figure 7 The scatter plot shown.

[0077] Specifically, attached Figure 7 This is a scatter plot of the trajectories of 20 targets. Each point represents the target's geocentric rectangular coordinates at a given moment. The x, y, and z axes represent the x, y, and z directions of the target's position coordinates, respectively. All points are currently shown in blue, indicating that target point classification has not yet been performed. (Attached) Figure 7 As can be seen, multiple targets at different times have not yet been matched. The process of matching the same target at different times is called multi-target trajectory recognition. The results after recognition are attached. Figure 8 As shown.

[0078] Specifically, attached Figure 8 The scatter plot shown is related to the attached plot. Figure 7 The corresponding 20 targets at different times are obtained by using the aforementioned algorithm in this application to identify and classify the positions of the same target at different times. Points of the same color are the position coordinates of the same target at different times. The colors of label0 to label19 represent targets 1 to 20, respectively.

[0079] In some embodiments, in step s5, an unlabeled dataset is divided into several groups or clusters using the HDBSCAN clustering algorithm or the spectral clustering algorithm. Based on unsupervised learning, the data points within the same cluster have high similarity to each other, while the data points in different clusters have low similarity; thus achieving target trajectory recognition.

[0080] Example 2:

[0081] This application also provides a post-test range multi-target trajectory measurement system, which adopts the post-test range multi-target trajectory measurement method as described above. The system includes multiple photoelectric theodolites at different stations working in concert. The photoelectric theodolites include: a tracking frame, an optical system, an image detector, an image processing and display system, an image storage system, a servo control system, and a data communication system.

[0082] Example 3:

[0083] This application also provides a storage medium storing program files capable of implementing the above-described post-target range multi-target trajectory measurement method.

[0084] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disk, etc.

[0085] Example 4:

[0086] This application also provides a processor for running a program, wherein the program executes the above-described post-target range multi-target trajectory measurement method during runtime.

[0087] In the embodiments of this application, the post-target range multi-target trajectory measurement method can be implemented by corresponding hardware or software units. Each unit can be an independent hardware or software unit, or it can be integrated into a single hardware or software unit, which is not intended to limit this application. The specific implementation of each unit can be referred to the description of Embodiment 1, and will not be repeated here.

[0088] In summary, based on existing photoelectric theodolite observation technology, this application utilizes the single-target intersection positioning concept to perform similar processing on multi-target observation images. First, it extracts the miss distance information of multiple targets, then directly performs intersection positioning calculations on data from multiple stations simultaneously. The results are filtered by using eccentric ray distance and intersection accuracy to eliminate false targets generated by combined intersections, obtaining the three-dimensional coordinate data of multiple targets at each time point. High-dimensional data reduces data overlap and facilitates data separation. Target trajectory separation is achieved through clustering algorithms, reducing algorithm complexity and improving target measurement efficiency.

[0089] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for post-target range multi-target trajectory measurement, characterized in that, The method includes the following steps: s1: Target tracking, organizing multiple photoelectric theodolites at different stations to work together to track and measure multiple targets, acquire target images, and form a sequence; s2: Multi-target miss distance extraction. Based on the pixel position of the target in the image, calculate the pixel position of its deviation from the image center, which is used for subsequent calculation of the target's azimuth and elevation angles. s3: Target azimuth and elevation angle calculation, which calculates the target's direction relative to the photoelectric theodolite based on the miss distance, detector intrinsic parameters, and theodolite encoder value; s4: Combined intersection positioning, which calculates and filters the intersection positioning of multiple target points from different stations at the same time to obtain the position coordinates of all targets at the current time; s5: Multi-target trajectory recognition. After completing the combined intersection and localization calculation in all images, the clustering algorithm is used to complete the trajectory recognition of the same target at different times. In step s4, the intersection and positioning calculation is performed using the non-plane intersection method; false targets are eliminated by using the non-plane ray distance and intersection accuracy to obtain the three-dimensional position coordinates of each target at each moment; among them, when different stations observe the same target at the same time, the distance between the principal optical axis rays of the stations is the shortest, and different targets are separated. In step s5, based on the fact that there are no strong abrupt changes during the movement of the same target, the movement trajectory is relatively smooth and has a certain regularity; and there is a certain distance between different targets; a clustering algorithm is used to divide a set of unlabeled datasets into several groups or clusters. Based on unsupervised learning, the data points within the same group or cluster have high similarity to each other, while the data points in different groups or clusters have low similarity; the data is grouped without any guidance to achieve target trajectory recognition.

2. The method for post-target range multi-target trajectory measurement as described in claim 1, characterized in that, Step s1 includes: s11: Before tracking begins, the photoelectric theodolite is positioned and leveled according to the actual observation. s12: During the tracking process, the servo control system controls the rotation of the tracking frame of the photoelectric theodolite. The target optical signal reaches the image detector through the optical system and is converted into a digital signal that is easy to process. The signal is then sent to the image processing and display system for real-time display by the data communication system. The operator makes real-time adjustments to the servo control based on the displayed image to achieve stable tracking of the target. s13: The data communication system sends the digital signal converted by the image detector to the image storage system for storage, so as to be used for subsequent trajectory separation.

3. The method as described in claim 2, characterized in that, Step s1 further includes: during the target tracking process, the photoelectric theodolite uses the encoder subsystem to record the azimuth and pitch angle of the photoelectric theodolite itself in real time.

4. The method for post-target range multi-target trajectory measurement as described in claim 1, characterized in that, The relationship between the target azimuth and elevation angles and the miss distance is defined as follows: Under ideal conditions, the target is located at the center of the image plane during tracking. At this time, the angle of rotation of the theodolite is the azimuth and elevation angles of the target relative to the theodolite. However, due to the irregularity of the target's movement and the lag in operation, the target often deviates from the center of the image plane. As a result, the real-time recorded rotation angle of the theodolite cannot be directly used as the target azimuth and elevation angles. It is necessary to correct for the target's deviation from the center on the image based on the distances ∆X and ∆Y of the target's deviation from the center. This distance of deviation from the center is the miss distance.

5. The method for post-target range multi-target trajectory measurement as described in claim 3, characterized in that, Step s2 includes: s21: Preprocessing of the image, including non-linear stretching, denoising, sharpening, and enhancement; s22: The Ostu thresholding method is used to calculate the image threshold, and the image threshold is used to segment the original image to complete the separation of the target and the background; s23: Extract the pixel position of the target and calculate the pixel position of the target that is offset from the center of the image based on the pixel position of the target in the image.

6. The method for post-target range multi-target trajectory measurement as described in claim 5, characterized in that, Step s3 includes: s31: Based on the focal length and pixel size of the optical system in the photoelectric theodolite, calculate the angle corresponding to one pixel interval in the image when the optical center of the optical system is the vertex; s32: Based on the angle corresponding to the pixel interval and the miss distance, the angle of the target deviating from the optical center is obtained, thereby completing the correction of the azimuth pitch angle.

7. A post-target range multi-target trajectory measurement system, characterized in that, The system employs the post-test range multi-target trajectory measurement method as described in any one of claims 1-6. The system includes multiple photoelectric theodolites at different stations working collaboratively. The photoelectric theodolites include: a tracking system, an optical system, an image detector, an image processing and display system, an image storage system, a servo control system, and a data communication system.

8. A storage medium, characterized in that, The storage medium stores program files capable of implementing the post-target range multi-target trajectory measurement method as described in any one of claims 1 to 6.

9. A processor, characterized in that, The processor is used to run a program, wherein the program executes the post-target range multi-target trajectory measurement method according to any one of claims 1 to 6.

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