Afterwards target range multi-target trajectory measurement method and system, storage medium and processor

Abstract

The invention is applicable to the technical field of target range target measurement, and provides a post-event target range multi-target trajectory measurement method and system, a storage medium and a processor. Firstly, combined intersection calculation of targets at all moments is completed through azimuth pitch angles of the targets relative to multiple observation stations, three-dimensional position coordinates of the multiple targets at all moments are obtained, dimension raising operation of data to be separated is achieved, and due to the fact that one-dimensional data is additionally introduced, intersection of the target data is greatly reduced, and the trajectory separation difficulty is lowered; meanwhile, an unsupervised learning method, namely a clustering algorithm, is introduced, the algorithm can group the data without any guidance, and the efficiency is extremely high. The afterward target range multi-target trajectory measurement system adopting the method also has the above effects.

Description

Technical Field

[0001] This application belongs to the technical field of range target measurement, and particularly relates to a method, system, storage medium and processor for measuring the trajectories of multiple targets in a range after the event. Background Art

[0002] At present, an optoelectronic theodolite is an optical measurement device that can automatically track the motion trajectory of a target and record the angular change value and image information of the target relative to the measurement reference. It makes the target within the field of view and tries to image it at the center through single-pole control or data guidance in two directions, azimuth and elevation. At the same time, the image is stored and recorded for trajectory calculation.

[0003] The existing method for measuring the target trajectory using an optoelectronic theodolite is mainly the multi-station intersection positioning method. As shown in the schematic diagram attached Figure 1 Multiple optoelectronic theodolites are used to observe the target simultaneously, and then intersection calculations are performed using the image data, recorded azimuth and elevation angle data, and the station addresses of each theodolite to obtain the position coordinates of the target at each moment, that is, the target motion trajectory. However, for multi-target measurement, there are multiple targets in each image. For several images at the same moment from different stations, it is impossible to determine which are the same targets. For images at different moments from the same station, it is also impossible to determine which are the same targets. Therefore, it is impossible to directly perform intersection calculations on the data at the same moment to obtain the target trajectory as in single-target measurement, which brings difficulties to multi-target trajectory measurement.

[0004] Among them, the measurement system uses the optoelectronic theodolite to track and measure the azimuth and elevation angles of the target relative to the station solved from the image. First, it is necessary to identify the same target at different moments in a single station, and then perform combined intersection positioning. However, the azimuth angle and elevation angle are two-dimensional data, and there are many data intersection points between different targets, resulting in a high misjudgment rate during separation.

[0005] At the same time, the separation uses a method of traversing all target data for fitting, and several sets of guiding points need to be given before fitting. The separation process is inefficient and has a low level of automation. There are deficiencies in the prior art. Summary of the Invention

[0006] The purpose of this application is to provide a method, system, storage medium and processor for measuring the trajectories of multiple targets in a range after the event, aiming to solve the technical problem that it is impossible to determine which are the same targets in multi-target measurement when using an optoelectronic theodolite to measure the target trajectory in the prior art.

[0007] On the one hand, this application provides a method for measuring the trajectories of multiple targets in a range after the event. The method includes the following steps: s1. Target tracking: Organize multiple optoelectronic theodolites at different stations to work together to track and measure multiple targets, obtain the images of the targets, and form a sequence; S2. Multi - target miss distance extraction: According to the pixel position of the target in the image, calculate its pixel position deviating from the center of the image for subsequent calculation of the target azimuth and elevation angles. S3. Target azimuth and elevation angle calculation: Calculate the direction of the target relative to the optoelectronic theodolite according to the miss distance value, the internal parameters of the detector, and the encoder value of the theodolite. S4. Combined intersection positioning: Perform intersection positioning calculation and screening on multiple target points at different stations at the same time to obtain the position coordinates of all targets at the current moment. S5. Multi - target trajectory recognition: After the combined intersection positioning calculation is completed for all images, use the clustering algorithm to complete the trajectory recognition of the same target at different times.

[0008] On the other hand, the present application also provides a post - range multi - target trajectory measurement system, which adopts the above - mentioned post - range multi - target trajectory measurement method. The system includes multiple optoelectronic theodolites at different stations working in cooperation; the optoelectronic theodolite includes: 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.

[0009] On the other hand, the present application also provides a storage medium, and the storage medium stores a program file capable of implementing the above - mentioned post - range multi - target trajectory measurement method.

[0010] On the other hand, the present application also provides a processor, and the processor is used to run a program. When the program runs, it executes the above - mentioned post - range multi - target trajectory measurement method.

[0011] The post - range multi - target trajectory measurement method of the present application changes the idea of "separation first and then intersection" and performs the processing of "intersection first and then separation". First, use the different azimuth and elevation angles of the target relative to multiple stations to complete the combined intersection calculation of the target at each moment, calculate the three - dimensional target position information using the two - dimensional azimuth and elevation angle data, and use the fact that the distance between the main optical axis rays of the stations is the shortest when observing the same target at the same time to separate different targets. Obtain the three - dimensional position coordinates of multiple targets at each moment and realize the dimensionality - raising operation of the data to be separated. Since one more dimension of data is introduced, the intersection of each target's data is greatly reduced, reducing the difficulty of trajectory separation; at the same time, an unsupervised learning method - the clustering algorithm is introduced. This algorithm can group data without any guidance and has extremely high efficiency. The post - range multi - target trajectory measurement system adopting this method also has the above - mentioned effects. Brief Description of the Drawings

[0012] Figure 1 It is a schematic diagram of single - target multi - station intersection positioning referred to by the post - range multi - target trajectory measurement method of the present application. Figure 2 It is the composition architecture diagram of the multi-target trajectory measurement system in the post-test range of this application; Figure 3 It is the detailed working flowchart of the multi-target trajectory measurement method in the post-test range of this application; Figure 4 It is the schematic diagram of the target tracking and measurement principle of the multi-target trajectory measurement method in the post-test range of this application; Figure 5 It is the schematic diagram of the principle for extracting the target miss distance in the multi-target trajectory measurement method in the post-test range of this application; Figure 6 It is the schematic diagram of the principle of the intersection positioning algorithm in the multi-target trajectory measurement method in the post-test range of this application; Figure 7 It is the multi-target combined intersection positioning result diagram during the application process of the multi-target trajectory measurement method in the post-test range of this application; Figure 8 It is the trajectory recognition result diagram of the spectral clustering method during the application process of the multi-target trajectory measurement method in the post-test range of this application; Figure 9 It is the implementation flowchart of the multi-target trajectory measurement method in the post-test range of this application. Specific Embodiments

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, the following further details this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0014] The following describes the specific implementation of this application in detail in combination with specific embodiments: Embodiment 1: Figure 9 It shows the implementation process of the multi-target trajectory measurement method provided in Embodiment 1 of this application. For the convenience of description, only the parts related to the embodiments of this application are shown and are described in detail as follows: On the one hand, this application provides a multi-target trajectory measurement method in the post-test range, and the method includes the following steps: s1. Target tracking, organizing multiple photoelectric theodolites at different measurement stations to work collaboratively, tracking and measuring multiple targets, obtaining images of the targets, and forming a sequence; s2. Extraction of multi-target miss distance, calculating the pixel position of the target deviating from the center of the image according to the pixel position of the target in the image for subsequent calculation of the target azimuth and elevation angles; s3. Calculation of target azimuth and elevation angles, calculating the direction of the target relative to the photoelectric theodolite according to the miss distance value, the internal parameters of the detector, and the theodolite encoder value; S4. Combined intersection positioning: Perform intersection positioning calculations and screening on multiple target points at different measurement stations at the same time to obtain the position coordinates of all targets at the current moment; S5. Multi-target trajectory recognition: After the combined intersection positioning calculations are completed for all images, use the clustering algorithm to complete the trajectory recognition of the same target at different times.

[0015] The process of applying the above method to the measurement system can be seen in the appendix Figure 3 as shown. In this application, data combination intersection is used at the same moment, and false targets are eliminated using the distance between skew lines. Then, the clustering algorithm is used to complete trajectory recognition, and three-dimensional data information is used for trajectory recognition, reducing the situation where there are many overlapping data generated when using two-dimensional data of azimuth and elevation angles for recognition in the past, resulting in difficult data separation.

[0016] As shown in the appendix Figure 4 The steps of realizing target tracking in step S1 include the following steps: S11. Before the start of tracking, according to the actual observation situation, select a location for the theodolite and complete the leveling operation; S12. During the tracking process, the servo control system controls the rotation of the tracking frame of the theodolite. The target optical signal reaches the image detector through the optical system and is converted into a digital signal convenient for processing, which is sent to the image processing and display system by the data communication system for real-time display; the operator makes real-time adjustment decisions on the servo control according to the displayed image situation to achieve stable tracking of the target; S13. The data communication system also sends the digital signal converted by the image detector to the image storage system for storage for subsequent use in post-event trajectory separation.

[0017] Furthermore, step S1 further includes: During the target tracking process of the theodolite, the encoder subsystem is used to record the azimuth and elevation angles of the rotation of the theodolite itself in real time.

[0018] Furthermore, as shown in the appendix Figure 5 The relationship between the target azimuth and elevation angles and the miss distance is defined as follows: Under ideal conditions, during the tracking process, the target is at the center position of the image plane. At this time, the angle of rotation of the theodolite is the azimuth angle and elevation angle of the target relative to the theodolite; however, due to the irregularity of the target movement and the lag of the operation, the target often deviates from the center of the image plane, resulting in the fact that the angle of rotation of the theodolite recorded in real time cannot be directly used as the target azimuth and elevation angles. It needs to be corrected according to the distances ∆X and ∆Y of the target from the center on the image, and this deviation distance is the miss distance.

[0019] Furthermore, step S2 includes: S21. Perform preprocessing on the image, including non-linear stretching, denoising, sharpening, and enhancement; S22. Calculate the image threshold using the Ostu threshold segmentation method, and use the image threshold to perform segmentation processing on the original image to complete the separation of the target and the background; S23. Extract the pixel position where the target is located, and calculate the pixel position where it deviates from the center of the image according to the pixel position of the target in the image.

[0020] Specifically, the key to extracting the miss distance is to obtain the centroid position of the target. During the process of the photoelectric theodolite tracking the target, due to the existence of a lot of noise interference, it is necessary to first preprocess the image in a suitable way according to the actual situation. The preprocessing methods include nonlinear stretching, denoising, sharpening, enhancement, etc.; then use the method based on threshold segmentation to extract the target. This application preferably uses the Ostu threshold segmentation method to calculate the image threshold, and uses this threshold to perform segmentation processing on the original image to separate the target and the background, and extract the pixel position where the target is located. The threshold segmentation formula is as follows: ; is the gray value at the pixel coordinates after image segmentation at the position, is the gray value at the pixel coordinates of the original image at the position; is the threshold, calculated using the Ostu threshold segmentation method, and the pixel positions greater than the threshold are the target positions. Among them, the pixel coordinates of the segmented image are the same as those of the original image.

[0021] Finally, calculate the centroid position according to the target pixel information, and the centroid calculation formula is as follows: ; ; Among them, U is the total number of columns of the image, and V is the total number of rows of the image data.

[0022] Take the pixel coordinates of the center position of the image as , then the calculation formula of the miss distance is as follows: .

[0023] Further, the step S3 includes: S31. Calculate the angle corresponding to one pixel interval in the image when the vertex is the optical center of the optical system according to the focal length and pixel size of the optical system in the photoelectric theodolite; S32. Based on the angle corresponding to the pixel interval and the miss distance, obtain the angle by which the target deviates from the optical center, and then complete the correction of the azimuth and elevation angles.

[0024] Specifically, after the miss distance is extracted in step S2, the rotation angle of the theodolite recorded in real time can be corrected to obtain the true angle of the target relative to the theodolite.

[0025] From the focal length of the theodolite optical system and the pixel size, the angle corresponding to one pixel interval of the image can be calculated when the vertex is the optical center of the optical system. As follows: ; Among them, is the focal length of the optical system, is the pixel size.

[0026] Combined with the miss distance from the above formula, the angle by which the target deviates from the optical center can be further obtained, thereby completing the correction of the azimuth and elevation angles. Assuming that the azimuth angle of the optoelectronic theodolite rotation is , and the elevation angle is , then the calculation formula for the final azimuth and elevation values of the target relative to the optoelectronic theodolite is as follows: ; where A is the azimuth angle of the target relative to the optoelectronic theodolite, and E is the elevation angle of the target relative to the optoelectronic theodolite.

[0027] Furthermore, as shown in Appendix Figure 6 , in step S4 of realizing combined intersection positioning, the intersection positioning calculation is performed using the off-plane intersection method; and false targets are eliminated by the off-plane ray distance and intersection accuracy to obtain the position coordinates of each target at each moment.

[0028] In Appendix Figure 6 inside is the station position, and the line segment is the common perpendicular of the two off-plane rays and , that is, the connecting line that makes the off-plane rays and the closest in space. Select a point M on this line segment as the target position obtained by intersection positioning. is the ray starting from the station with the direction of the azimuth and elevation angles .

[0029] Specifically, the intersection positioning uses the off-plane intersection method. In the O-xyz space rectangular coordinate system in Appendix Figure 6 , given the coordinates ( ) of the optoelectronic theodolite ), the azimuth angle measured for the target M is , and the elevation angle is , and the formed space line is . Theoretically speaking, the rays and They can intersect at point M. However, due to the influence of the structure and imaging mechanism of the optoelectronic theodolite, the angular measurement accuracy, time synchronization, differences in the tracked target parts, and the working environment, Figure 6 in and cannot intersect, that is, when the two optoelectronic theodolites observe the same target, their main optical axes do not intersect and present a skew relationship.

[0030] At this time, based on the position coordinates of the two optoelectronic theodolites and the measured azimuth and elevation angles, the line segment equation of is derived. Also, ⊥ , where the line segment is a part of the ray ; the coordinate value of can be obtained. Then, a point is selected on its common perpendicular according to the angular measurement accuracy of the two theodolites as an estimate of the true position of the target. The selection of point is controlled by the weighting coefficient in the following formula, and the theodolite with higher angular measurement accuracy is assigned a higher weight. For example, the last three equalities in the following intersection formula have the weighting coefficient , and the position of point M is adjusted through this coefficient. For example, if the accuracy of the first measurement station is higher, then is assigned a larger value, and the coefficient (1 - ) of the second measurement station is smaller.

[0031] The coordinate calculation of the skew intersection method is as follows: ; In the above formula: is the weighting coefficient appropriately selected according to the different angular measurement accuracies of each theodolite, and the skew ray distance is the length, is the line segment and the line segment angle. This application uses intersection first and then separation to achieve dimension elevation, reduce the intersection points, lower the misjudgment rate, and apply the clustering algorithm to ballistic separation instead of fitting to improve the separation efficiency.

[0032] When measuring multiple targets, there are multiple sets of azimuth and elevation values at each measurement station at the same time. At this time, it is impossible to judge which data belong to the same target, so it is necessary to perform combined intersection on the azimuth and elevation data at the same time in combination with the station address of the measurement station. During the intersection process, many false targets will be generated, and this application eliminates them (false targets) through the skew ray distance d.

[0033] The key point of the above embodiments of the present application is to use the flight data combination intersection of multiple flying targets at the same moment, and use the off-plane ray distance to eliminate false targets; then use the clustering algorithm to complete trajectory recognition, and use three-dimensional data information for trajectory recognition, reducing the situation where it is difficult to separate data due to a lot of overlap in the previous use of two-dimensional data of azimuth and elevation angles.

[0034] Specifically, according to the principle of off-plane intersection positioning method, when two theodolites observe the same target point at the same moment, the off-plane ray distance is the smallest; when observing different targets, that is, using the azimuth angles and elevation angles of different targets at the same moment for intersection calculation, due to the mismatch of angle information, even in the most ideal state without any errors, the rays formed by the two measurement stations and their respective azimuth angles and elevation angles will not intersect at a point, and the off-plane ray distance d will be very large. Therefore, the intersection calculation can be carried out by arranging and combining the azimuth and elevation angle information of different targets at the same moment, and the combination with the smallest off-plane ray distance is the data corresponding to the same target of different measurement stations at the same moment, and the position coordinates obtained by the intersection of other target data are false targets.

[0035] Among them, the logic of the combination is to perform intersection calculations between the azimuth and elevation angles of target 1 at measurement station 1 and the azimuth and elevation angles of all targets at measurement station 2 respectively, and find a set of data with the smallest off-plane straight line distance. This data is the azimuth and elevation angles of the target observed at measurement station 2 at this moment that is the same as the target at measurement station 1 (target 1). Other targets also find the matching data of the two measurement stations in this way for intersection calculation.

[0036] Furthermore, in step s5, based on the fact that there will be no strong mutations 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 an unlabeled data set into several groups or clusters, so that the data points within the same cluster are relatively similar to each other, while the data points in different clusters are less similar; realizing target trajectory recognition.

[0037] Specifically, after obtaining the position coordinates of each target at each moment, a scatter plot as shown in the appendix can be obtained. Figure 7 as shown.

[0038] Specifically, the appendix Figure 7 is a scatter plot composed of the trajectories of 20 targets. Each point represents the geocentric rectangular coordinates of the target at a certain moment. The x, y, and z axes represent the xyz directions of the target position coordinates respectively. At this time, all points are represented in blue, indicating that the target points have not been classified yet. In the appendix Figure 7 it can be seen that the multi-targets at different moments have not been matched yet. The process of matching the same target at different moments is multi-target trajectory recognition. The recognition result is as shown in the appendix Figure 8 as shown.

[0039] Specifically, the Figure 8 scatter plot shown is the geocentric rectangular coordinates of 20 target objects at different times corresponding to the Figure 7 . Using the aforementioned algorithm in this application to identify and classify the positions of the same target at different times, the points of the same color are the position coordinates of the same target at different times, and the colors from label0 to label19 represent target 1 to target 20 respectively.

[0040] In some embodiments, in step s5, the HDBSCAN clustering algorithm or the spectral clustering algorithm is used to divide an unlabeled dataset into several groups or clusters, so that the data points within the same cluster are highly similar to each other based on unsupervised learning, while the data points in different clusters are less similar; to achieve target trajectory recognition.

[0041] Embodiment 2: This application also provides a post-event range multi-target trajectory measurement system, which adopts the post-event range multi-target trajectory measurement method as described above. The system includes multiple photoelectric theodolites at different measurement stations that work together; the photoelectric theodolite includes: 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.

[0042] Embodiment 3: This application also provides a storage medium, and the storage medium stores a program file that can implement the above-mentioned post-event range multi-target trajectory measurement method.

[0043] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disc, etc.

[0044] Embodiment 4: This application also provides a processor, and the processor is used to run a program. Among them, when the program runs, it executes the above-mentioned post-event range multi-target trajectory measurement method.

[0045] In the embodiments of this application, the post-event range multi-target trajectory measurement method can be implemented by corresponding hardware or software units. Each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not limited in this application. The specific implementation manners of each unit can refer to the description of Embodiment 1 and will not be elaborated here.

[0046] In summary, based on the existing photoelectric theodolite observation technology, the present application uses the idea of single-target intersection positioning to perform similar processing on multi-target observation images. First, the miss distance information of multi-targets is extracted, and then the intersection positioning calculation is directly performed on the data of multiple stations at the same time. The results are screened by the distance between skew rays and the intersection accuracy to eliminate the false targets generated by combined intersection, and the three-dimensional coordinate data of multi-targets at each moment are obtained. The high-dimensional data reduces the intersection between data for easy data separation; the target trajectory is separated by a clustering algorithm to reduce the algorithm complexity and improve the target measurement efficiency.

[0047] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for measuring the trajectories of multiple targets in a post-event shooting range, characterized in that The method includes the following steps: S1: Target tracking. Organize multiple optoelectronic theodolites at different measurement stations to work collaboratively, track and measure multiple targets, obtain the images of the targets, and form a sequence. S2: Extraction of miss distances of multiple targets. According to the pixel positions of the targets in the images, calculate the pixel positions where they deviate from the image center for subsequent calculation of the azimuth and elevation angles of the targets. S3: Resolution of target azimuth and elevation angles. Calculate the direction of the target relative to the optoelectronic theodolite based on the miss distance values, the internal parameters of the detector, and the encoder values of the theodolite. S4: Combined intersection positioning. Perform intersection positioning calculations and screening on multiple target points at different measurement stations at the same time to obtain the position coordinates of all targets at the current moment. S5: Identification of multi-target trajectories. After the combined intersection positioning calculations are completed for all images, use the clustering algorithm to complete the trajectory identification of the same target at different times.

2. The method for measuring multi-target trajectories in a post-event range as described in claim 1, characterized in that, The step S1 includes: S11: Before the tracking starts, select the location of the optoelectronic theodolite according to the actual observation situation and complete the leveling operation. S12: During the tracking process, the servo control system controls the rotation of the tracking frame of the optoelectronic theodolite. The target optical signal reaches the image detector through the optical system and is converted into a digital signal convenient for processing, which is sent by the data communication system to the image processing and display system for real-time display. The operator makes real-time adjustment decisions on the servo control according to the displayed image situation 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 for subsequent use in post-event trajectory separation.

3. The method according to claim 2, characterized in that The step S1 further includes: During the target tracking process, the optoelectronic theodolite uses the encoder subsystem to record the azimuth and elevation angles of the rotation of the optoelectronic theodolite itself in real time.

4. The method for measuring the trajectories of multiple targets in a post-event shooting range according to claim 1, characterized in that, The relationship between the target azimuth and elevation angles and the miss distance is defined as: Under ideal conditions, during the tracking process, the target is at the center position of the image plane. At this time, the angle of rotation of the theodolite is the azimuth angle and elevation angle of the target relative to the theodolite. However, due to the irregularity of the target movement and the lag of the operation, the target often deviates from the center of the image plane, resulting in the fact that the angle of rotation of the theodolite recorded in real time cannot be directly used as the target azimuth and elevation angles. It is necessary to correct according to the distances ∆X and ∆Y where the target deviates from the center on the image. This deviation distance is the miss distance.

5. The method for measuring multi-target trajectories in a post-event range according to claim 3, wherein The step S2 includes: S21: Perform preprocessing on the images, including non-linear stretching, denoising, sharpening, and enhancement. S22: Use the Ostu threshold segmentation method to calculate the image threshold, and use the image threshold to perform segmentation processing on the original image to complete the separation of the target and the background. S23: Extract the pixel positions where the targets are located, and according to the pixel positions of the targets in the images, calculate the pixel positions where they deviate from the image center.

6. The method for measuring multi-target trajectories in a post-event shooting range according to claim 5, characterized in that, The step S3 includes: S31: According to the focal length and pixel size of the optical system in the optoelectronic theodolite, calculate the angle corresponding to one pixel interval in the image when the vertex is the optical center of the optical system. S32: Based on the angle corresponding to the pixel interval and the miss distance, obtain the angle by which the target deviates from the optical center, and then complete the correction of the azimuth and elevation angles.

7. The method for measuring multi-target trajectories in a post-event shooting range according to claim 1, wherein, In the step s4, the intersection positioning calculation is performed by using the off-plane intersection method; and false targets are eliminated by the off-plane ray distance and the intersection accuracy, and the position coordinates of each target at each moment are obtained.

8. The post - range multi - target trajectory measurement method according to claim 1, wherein, In the step s5, based on the fact that there will be no strong mutation during the movement of the same target, the movement trajectory is relatively smooth and there is a certain regularity; and there is a certain distance between different targets; a clustering algorithm is used to divide a set of unlabeled data sets into several groups or clusters, and based on unsupervised learning, the data points within the same group or cluster are highly similar to each other, while the data points of different groups or clusters are less similar; Target trajectory recognition is realized.

9. A multi-target trajectory measurement system for a post-event shooting range, characterized in that, The post-range multi-target trajectory measurement method according to any one of claims 1-8 is adopted. The system includes multiple photoelectric theodolites at different measuring stations working in cooperation; the photoelectric theodolite includes: tracking, 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.

10. A storage medium, characterized in that, The storage medium stores a program file capable of implementing the post-range multi-target trajectory measurement method according to any one of claims 1 to 8.

11. A processor, characterized in that, The processor is used to run the program, wherein the program, when running, executes the post-range multi-target trajectory measurement method according to any one of claims 1 to 8.

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