Passive calibration method, system and equipment for dynamic target and medium

Abstract

The invention relates to the technical field of photoelectric detection and radars, in particular to a calibration method, system and device for a radar and a laser anti-collision device and a medium, and aims to track and lock the same dynamic non-cooperative target through the laser anti-collision device and the phased array radar. Synchronously acquiring spherical coordinate observation data of the dynamic targets in the public view field under respective coordinate systems; and after the spherical coordinates are converted into rectangular coordinates, calculating a point set centroid, constructing a covariance matrix and performing singular value decomposition based on a rigid body transformation model, and directly solving a rotation matrix and a translation vector from a radar coordinate system to a laser anti-equipment-free coordinate system, thereby completing calibration. No external absolute coordinate reference is needed, only relative observation data of equipment is utilized, and the method has the advantages of being rapid in deployment, high in environmental adaptability, low in cost and high in calibration speed. The problems that in the prior art, a calibration system depends on RTK or a high-precision map, the environmental adaptability is poor, and the deployment requirement is high are solved.

Description

A passive calibration method, system, device, and medium for dynamic targets. Technical Field

[0001] This invention relates to the field of photoelectric detection and radar technology, and in particular to a passive calibration method, system, device and medium for dynamic targets. Background Technology

[0002] Laser-based anti-drone equipment uses high-energy lasers to burn away drones in the air, achieving its purpose of attacking them. In a defense system primarily based on phased array radar, laser-based anti-drone equipment (a high-energy laser system used to strike drones) often needs to work in conjunction with the phased array radar. The radar equipment is responsible for detecting targets and then disseminating the target information to the laser-based anti-drone equipment for attack.

[0003] Since radar equipment and laser anti-dual equipment are physically separate and independently installed, their spatial positions and observation orientations are different. Therefore, it is necessary to calibrate their coordinate systems to establish a unified spatial reference so that the target coordinates provided by the radar can be accurately understood and pointed to by the laser anti-dual equipment.

[0004] Currently, the mainstream technical solution for calibrating such heterogeneous sensors is to install high-precision RTK (Real-Time Dynamic Carrier Phase Differential) positioning receivers on radar and laser anti-radio devices respectively. Through RTK, both can obtain their own high-precision absolute position and orientation in the Earth coordinate system. The calibration process is simplified to calculating the relative transformation relationship between two known absolute poses. However, this method has significant drawbacks: First, the performance of RTK is heavily dependent on the coverage of ground reference stations. In remote areas, at sea, or on the battlefield, where there is a lack of base station support, its positioning accuracy will drop sharply or even fail.

[0005] Secondly, RTK signals are easily affected by obstructions such as tall buildings and mountains, and can only achieve ideal accuracy in open and unobstructed environments, which greatly limits the system's deployment flexibility and environmental adaptability.

[0006] In addition, the introduction of RTK equipment and its supporting facilities has increased system costs and complexity.

[0007] In existing technologies, for example, Chinese patent application number 202210543378.0, entitled "A Method for Calibration of Extrinsic Parameters of Multi-LiDAR Based on Non-overlapping Field of View," discloses a method for solving the transformation parameters between sensors through singular value decomposition. In specific implementation, during multi-LiDAR calibration, a checkerboard calibration board of known size is manually moved, and point clouds are collected at different locations. An intermediate coordinate system is established using the known geometric information of the calibration board, and the transformation relationship is calculated through SVD decomposition. Although this method does not require additional RTK installation, it still relies on a cooperative target (calibration board) and manual measurement (translation distance), making the calibration process complex and unsuitable for scenarios where the target is difficult to deploy or the target is unknown.

[0008] To address the problems in the existing technology, this invention provides a passive calibration method, system, device, and medium for dynamic targets. Summary of the Invention

[0009] The purpose of this invention is to provide a passive calibration method, system, device, and medium for dynamic targets, in order to solve the technical problems of low deployment flexibility and poor environmental adaptability in the existing technology that uses external devices as "calibration boards" for reference.

[0010] The technical solution of this invention is: a passive calibration method for dynamic targets, comprising: radar equipment and laser anti-navigation equipment independently tracking and synchronously acquiring multiple sets of spatiotemporally matched observation data pairs of the target equipment, each data pair including a dataset in the radar coordinate system and a dataset in the laser anti-navigation equipment coordinate system; the dynamic target operating within the common detection field of view of the laser anti-navigation equipment and the radar equipment; performing coordinate system transformation on the dataset in the radar coordinate system and the dataset in the laser anti-navigation equipment coordinate system respectively, representing them as a first set of solution coordinate points and a second set of solution coordinate points for algorithm calculation; performing centroid removal processing on the first set of solution coordinate points and the second set of solution coordinate points respectively, and solving the rigid body transformation parameters of the phased array radar coordinate system and the laser anti-navigation equipment coordinate system through a singular value decomposition algorithm to obtain the coordinate transformation matrix used by the laser anti-navigation equipment and the radar equipment for joint calibration of the dynamic target.

[0011] Preferably, the radar equipment collects first spherical coordinate observation data of the dynamic target in the radar coordinate system, and the laser anti-radio device collects second spherical coordinate observation data of the dynamic target in the laser anti-radio device coordinate system; the first spherical coordinate observation data and the second spherical coordinate observation data are respectively converted into a first set of solution coordinate points in the radar rectangular coordinate system and a second set of solution coordinate points in the laser anti-radio device rectangular coordinate system.

[0012] Preferably, the first solution coordinate point set and the second solution coordinate point set are subjected to centroid removal processing, including: calculating the centroid of the first solution coordinate point set. and the centroid of the second solution coordinate point set The first and second solution coordinate point sets are decentroided respectively to obtain the first solution coordinate point set after centroid removal. and the second solution coordinate set after centroid removal ,in: ; ; This represents a point in the first solution coordinate set. This represents the set of coordinate points for the second solution.

[0013] Preferably, the rigid body transformation parameters from the phased array radar coordinate system to the laser anti-device coordinate system are solved using a singular value decomposition algorithm, including: constructing the covariance matrix H. Where n is the number of points in the point set; i represents the i-th point in the point set; singular value decomposition is performed on the covariance matrix H to obtain: ;in, Let represent a 3×3 orthogonal matrix, Σ represent a 3×3 diagonal matrix, and the singular values ​​are arranged in descending order. Represent a 3×3 orthogonal matrix; calculate the initial rotation matrix based on the results of the singular value decomposition. , and the initial rotation matrix The determinant is evaluated and corrected to obtain the corrected rotation matrix. Based on the centroid and the rotation matrix Calculate the translation vector , ; Obtain the coordinate transformation matrix used for calibration of laser anti-nuclear devices and phased array radar devices. , is represented as: .

[0014] Preferably, for the initial rotation matrix The determinant is judged and corrected, including: the initial rotation matrix is ,like Then adjust The last row, the adjusted matrix V is represented as : The corrected rotation matrix is: .

[0015] Each preferred set of first and second spherical coordinate observation data includes spherical coordinates at at least three different times, wherein the spherical coordinates include azimuth, elevation, and distance.

[0016] Preferably, the dynamic target is set as a non-cooperative target that can be simultaneously locked and tracked by the phased array radar and the laser anti-drone device, including any one of drones, birds or aircraft.

[0017] A passive calibration system for dynamic targets, used to implement a passive calibration method for dynamic targets, includes: a data acquisition device, including a radar device and a laser anti-navigation device, for synchronously tracking and locking the dynamic target and outputting observation data in their respective coordinate systems; a spatiotemporal synchronization module, for spatiotemporally aligning the acquired observation data to construct spatiotemporally matched observation data pairs; a data processing module, for receiving and processing the observation data pairs, calculating rigid body transformation parameters from the radar device coordinate system to the laser anti-navigation device coordinate system, and obtaining a coordinate transformation matrix for joint calibration; and a control module, for controlling the laser anti-navigation device to lock onto the dynamic target and execute strike actions based on the coordinate transformation matrix for joint calibration and the current observation data.

[0018] An electronic device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the passive calibration method for a dynamic target.

[0019] A computer-readable storage medium storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the passive calibration method for a dynamic target.

[0020] Compared with existing technologies, the advantages of this invention are: This invention directly uses an unknown dynamic target as a medium, eliminating the need for external facilities such as RTK or high-precision maps that provide absolute location information. This avoids calibration failures or accuracy degradation caused by missing base stations or signal obstruction, greatly improving the system's deployment and survivability in complex, harsh, or remote environments, resulting in good environmental adaptability. Furthermore, it eliminates the need for an external calibration board, reducing system maintenance costs.

[0021] The calibration method provided by this invention requires only a minimum of three pairs of data points to complete the calibration process, reducing the difficulty of the calibration operation, shortening the calibration time, and improving the calibration efficiency. Based on a mathematical model constructed using a dual coordinate system, this invention solves for the optimal rotation and translation parameters from multiple sets of synchronous observation data, effectively suppressing observation noise and ensuring the accuracy and reliability of the calibration results. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 is a schematic diagram of the application scenario and equipment layout of the calibration method of the present invention; Figure 2 is an overall flowchart of the rapid calibration method of the present invention; Figure 3 is a flowchart of the specific steps of solving rigid body transformation parameters based on singular value decomposition of the present invention; Figure 4 is a schematic diagram comparing the 3D trajectory of the calibration system of the present invention with that of the system composed of a laser, a turntable and a laser rangefinder; Figure 5 is a schematic diagram comparing the projection trajectory of the calibration system of the present invention with that of the system composed of a laser, a turntable and a laser rangefinder in the XY plane; Figure 6 is a schematic diagram comparing the projection trajectory of the calibration system of the present invention with that of the system composed of a laser, a turntable and a laser rangefinder in the XZ plane; Figure 7 is a schematic diagram comparing the distance error of each point between the calibration method of the present invention and the mean compensation calibration method; Figure 8 is a schematic diagram comparing the histogram of the distance error distribution between the calibration method of the present invention and the mean compensation calibration method. Detailed Implementation

[0023] The following detailed description of the present invention is provided in conjunction with specific embodiments: As shown in Figure 2, a passive calibration method for dynamic targets is provided. The dynamic target serves as the calibration object and moves arbitrarily within the common field of view of the laser anti-dual device and the radar device. The laser anti-dual device and the radar device lock onto and track the dynamic target respectively. The calibration of the dynamic target is achieved based on the conversion relationship of the data information (position and orientation) independently observed and collected by the two devices. This method does not rely on a calibration plate / target with known coordinates or known size. Specifically, the passive calibration method for dynamic targets is implemented through a passive calibration system for dynamic targets. This system includes: an acquisition device, a spatiotemporal synchronization module, a data processing module, and a control module.

[0024] The data acquisition device includes radar equipment and laser anti-gravity equipment. The structure of the laser anti-gravity equipment includes a spherical coordinate turntable, a camera, and a high-energy laser emitter. The relative positions of the camera and the laser emitter are fixed, and they are placed together on the turntable, which controls their movement (horizontal and pitch rotation).

[0025] The dynamic target is set to any object that can be locked and tracked by radar and laser anti-drone equipment, including but not limited to birds, civil aircraft, and drones.

[0026] In practice, the radar equipment uses a phased array radar, and the dynamic target is set as an unmanned aerial vehicle (UAV).

[0027] The radar equipment and the laser anti-drone equipment synchronously track and lock onto the dynamic target, and construct the UAV's position coordinates in their respective coordinate systems. The spatiotemporal synchronization module performs spatiotemporal alignment of the collected positions to construct spatiotemporally matched observation data pairs. The data processing module receives and processes the observation data pairs, calculates the rigid body transformation parameters from the phased array radar coordinate system to the laser anti-drone equipment coordinate system, and obtains the coordinate transformation matrix for joint calibration. Based on the coordinate transformation matrix for joint calibration and the current observation data, the control module controls the laser anti-drone equipment to burn the UAV in the air with high-energy laser to achieve the purpose of striking it.

[0028] Based on the passive calibration system and in conjunction with Figures 2 and 3, the passive calibration method for dynamic targets includes the following steps: Step 1, collect observation data pairs.

[0029] Referring to Figure 1, an application scenario and equipment layout diagram of this calibration method are provided. In this diagram, radar equipment and laser anti-dummy equipment track and identify dynamic targets respectively. The system achieves microsecond-level time synchronization through a high-precision clock (e.g., GPS PPS pulse) to ensure that the two devices collect observation data at the same time, forming a spatiotemporally matched observation data pair.

[0030] In the layout diagram 1 corresponding to this embodiment, This indicates the origin of the radar equipment's coordinate system. This represents the origin of the coordinate system for laser anti-reflection devices. This indicates the position of a dynamic target (i.e., a drone) in space. This represents the transformation matrix from the radar coordinate system to the inverse coordinate system, i.e., the parameters to be calibrated, including the rotation matrix R and the translation vector t.

[0031] After radar and anti-navigation equipment lock onto a target, the target coordinates obtained in their own coordinate system are usually represented in spherical coordinates (azimuth α, elevation β, and range d). The spherical coordinate representations are as follows: ; .

[0032] in, and The pitch angle in spherical coordinates. The azimuth angle in spherical coordinates. The distance to the target (slope distance). This represents the coordinates of the dynamic target in the radar coordinate system, where i represents the i-th coordinate point in both the radar coordinate system and the laser anti-dual device coordinate system.

[0033] The angle and distance information are converted into point coordinates in three-dimensional space to facilitate subsequent matrix operations. The converted coordinates in the Cartesian coordinate system are represented as follows: .

[0034] The calibration parameters that need to be solved are expressed as follows: .

[0035] The calibration parameter satisfies the formula: .

[0036] in, It is a 3x3 rotation matrix. It is a 3x1 translation vector.

[0037] Step 2, mass calculation and centroid removal.

[0038] The centroids corresponding to the data point sets observed by the radar equipment and the data point sets observed by the laser anti-nuclear equipment are respectively: ; .

[0039] The coordinates after centroid removal are represented as follows: ; .

[0040] By removing the centroid, the translation vector Separating the coordinates and decoupling them from rotation and translation, the coordinate transformation relationship simplifies to: The centroids of the two observation data point sets are aligned with the origin of their respective coordinate systems, leaving only a rotational relationship between the two sets.

[0041] Step 3: Construct the covariance matrix and solve for singular values.

[0042] Construct the covariance matrix H: .

[0043] Because actual measurements contain errors, the rotation matrix R is solved using the least squares method to obtain the rotated result. and Align as much as possible, that is, minimize error: The minimization problem is equivalent to the maximization problem: ;and ,in, .

[0044] For matrix H, we obtain the following through singular value decomposition (SVD): The optimal initial rotation matrix is ​​expressed as: .

[0045] in, It is a 3x3 orthogonal matrix. It is a 3x3 diagonal matrix (singular values ​​are arranged in descending order). It is a 3x3 orthogonal matrix.

[0046] Step 4: Solve for and correct the initial rotation matrix.

[0047] For the optimal initial rotation matrix ,like Then adjust The last line: The corrected rotation matrix is ​​expressed as: .

[0048] By correcting the matrix, we can ensure that the resulting matrix is ​​a true rotation matrix (determinant is +1), rather than a reflection matrix (determinant is -1).

[0049] Step 5: Solve for the translation vector to obtain the coordinate transformation matrix used for joint calibration.

[0050] After obtaining the rotation matrix, calculate the translation vector between the origins of the two coordinate systems, i.e., for the formula: Take the average of both sides (i.e., the centroid): ,but: .

[0051] Based on the calibration method provided above, the experimental calibration results are shown in Figures 4-8. Figure 4 is a 3D trajectory comparison diagram of the calibration system of this invention using radar calibration and the system using a camera + laser rangefinder. Figure 5 is a comparison diagram of the projected trajectories of the calibration system of this invention using radar calibration and the system using a camera + laser rangefinder. Figure 6 is a comparison diagram of the projected trajectories of the calibration system of this invention using radar calibration and the system using a camera + laser rangefinder. In Figures 4, 5, and 6, "dots" represent the coordinate points (sets) corresponding to the calibration using the camera + laser rangefinder system, and "triangles" represent the trajectories of the system of this invention converted from radar points to turntable points. From the overlap of the trajectories in the figures, it can be intuitively seen that the higher the overlap, the more accurate the calibration of this application.

[0052] Figure 7 is a schematic diagram comparing the distance error of each point in the calibration method of the present invention and the traditional mean compensation method. The upper curve is the distance error curve composed of the point set measured by the mean compensation method, and the lower curve is the distance error curve composed of the point set measured by the calibration method of the present invention. The upper horizontal dashed line represents the average distance error corresponding to the measurement data of the mean compensation method: 8.625799m, and the lower horizontal dashed line represents the average distance error corresponding to the measurement data of the calibration method of the present invention: 3.044992m. It can be seen that after calibration, the overall error is significantly reduced.

[0053] Figure 8 is a schematic diagram comparing the distance error of each point in the calibration method of the present invention and the traditional mean compensation method. It can be seen that after calibration, the error of most points in the coordinate points collected by the system is within a low range, and the calibration accuracy is high.

[0054] Based on error comparison, the statistical analysis results of distance error were obtained: the total number of coordinate point sets was 204, the average error was 3.044992m, the maximum error was 10.182122m, the minimum error was 0.189391m, the standard deviation of the error was 2.095885, the median error was 2.352932m, and the error distribution was as follows: there were 139 points with an error less than the average error, and 25 points with an error greater than twice the average error.

[0055] The calibration method provided by this invention uses a dynamic target (whose position, attitude, and velocity are all unknown and time-varying) as the calibration medium. It does not require absolute coordinate information provided by a "calibration board" with known coordinates and dimensions, such as GPS, RTK, or any other form, as a reference. The radar equipment and the laser anti-dummy equipment synchronously acquire the observation data of the dynamic target. All calculations are based solely on the observation data of the radar equipment and the laser anti-dummy equipment on the same dynamic target. The transformation relationship between the coordinate systems of the radar equipment and the laser anti-dummy equipment is directly solved through a mathematical model. The identification and detection process is decoupled from ground base stations or other auxiliary facilities, and has the advantages of rapid deployment, strong environmental adaptability, and low cost.

[0056] By comparing the trajectory and error in the experimental results, the calibration method of the present invention has a small deviation from the theoretical prediction point (in the camera image), and the calibration results have the characteristics of high precision and high reliability.

[0057] During the target tracking and calibration process, only a minimum of 3 sets of calibration data are needed to complete the calibration, reducing the difficulty and time required for calibration, making the calibration more efficient.

[0058] During actual monitoring, the radar equipment detects the coordinate data of dynamic targets, transforms the acquired coordinates into the coordinate system of the laser anti-dual equipment through a coordinate transformation matrix, and guides the laser to emit light to accurately strike the target.

[0059] This invention also provides an electronic device, which includes a processor and a memory; the memory stores one or more instructions, which are adapted for the processor to load and execute, to implement a passive calibration method for a dynamic target as described in the above method embodiments.

[0060] Memory is used to store software programs and modules. The processor executes these stored software programs and modules to perform various functional applications and data processing. Memory mainly includes a program storage area and a data storage area. The program storage area stores the operating system, application programs required for functions, etc.; the data storage area stores data created based on device usage, etc. Furthermore, memory may include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory may also include a memory controller to provide the processor with access to the memory.

[0061] The internal structure of the electronic device provided in the embodiments of the present invention may include, but is not limited to, a processor, a memory, and a communication interface. The processor, memory, and communication interface in the electronic device may be connected by a bus or other means. In the embodiments of this specification, a connection via a bus is taken as an example.

[0062] The processor (or CPU, Central Processing Unit) is the computing and control core of the electronic device. A communication interface is used for communication between the memory and the processor. The memory stores programs and data. It is understood that the memory here can be a high-speed RAM storage device, or a non-volatile memory device, such as at least one disk storage device; optionally, it can also be at least one storage device located remotely from the aforementioned processor. The memory provides storage space, which stores the operating system of the electronic device, and may include, but is not limited to, Windows (an operating system), Linux (an operating system), etc. This invention does not limit this; furthermore, the storage space also stores computer programs (including program code) suitable for loading and execution by the processor. In the embodiments of this specification, the processor loads and executes the computer program stored in the memory to implement the passive calibration method for a dynamic target provided in the above method embodiments.

[0063] This invention also provides a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction, at least one program, code set, or instruction set related to implementing the passive calibration method for a dynamic target in the method embodiments. The at least one instruction, at least one program, code set, or instruction set can be loaded and executed by the processor of the electronic device to implement the passive calibration method for a dynamic target provided in the above method embodiments.

[0064] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0065] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments, while other embodiments fall within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than those shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0067] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0068] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A passive calibration method for dynamic targets, characterized in that, include: The radar equipment and the laser anti-navigation equipment independently track and synchronously acquire multiple sets of spatiotemporally matched observation data pairs of the target equipment. Each data pair includes a dataset in the radar coordinate system and a dataset in the laser anti-navigation equipment coordinate system. The dynamic target operates within the common detection field of view of the laser anti-navigation equipment and the radar equipment. The datasets in the radar coordinate system and the laser anti-navigation equipment coordinate system are respectively transformed into coordinate system sets, which are represented as a first set of solution coordinate points and a second set of solution coordinate points for algorithm calculation. The first set of solution coordinate points and the second set of solution coordinate points are respectively decentroided, and the rigid body transformation parameters between the phased array radar coordinate system and the laser anti-navigation equipment coordinate system are solved by the singular value decomposition algorithm to obtain the coordinate transformation matrix used by the laser anti-navigation equipment and the radar equipment for joint calibration of the dynamic target.

2. The passive calibration method for a dynamic target according to claim 1, characterized in that, The radar equipment collects first spherical coordinate observation data of the dynamic target in the radar coordinate system, and the laser anti-radio device collects second spherical coordinate observation data of the dynamic target in the laser anti-radio device coordinate system; the first spherical coordinate observation data and the second spherical coordinate observation data are respectively converted into a first set of solution coordinate points in the radar rectangular coordinate system and a second set of solution coordinate points in the laser anti-radio device rectangular coordinate system.

3. The passive calibration method for a dynamic target according to claim 2, characterized in that, Centroid removal is performed on both the first and second solution coordinate point sets, including: calculating the centroid of the first solution coordinate point set. and the centroid of the second solution coordinate point set The first and second solution coordinate point sets are decentroided respectively to obtain the first solution coordinate point set after centroid removal. and the second solution coordinate set after centroid removal ,in: ； ； This represents a point in the first solution coordinate set. This represents the set of coordinate points for the second solution.

4. The passive calibration method for a dynamic target according to claim 3, characterized in that, The rigid body transformation parameters from the phased array radar coordinate system to the laser anti-device coordinate system are solved using the singular value decomposition algorithm, including: constructing the covariance matrix H. Where n is the number of points in the point set; i represents the i-th point in the point set; singular value decomposition is performed on the covariance matrix H to obtain: ;in, Let represent a 3×3 orthogonal matrix, Σ represent a 3×3 diagonal matrix, and the singular values ​​are arranged in descending order. Represent a 3×3 orthogonal matrix; calculate the initial rotation matrix based on the results of the singular value decomposition. ， and the initial rotation matrix The determinant is evaluated and corrected to obtain the corrected rotation matrix. Based on the centroid and the rotation matrix Calculate the translation vector ， ; Obtain the coordinate transformation matrix used for calibration of laser anti-nuclear devices and phased array radar devices. , is represented as: 。 5. The passive calibration method for a dynamic target according to claim 4, characterized in that, For the initial rotation matrix The determinant is judged and corrected, including: the initial rotation matrix is ,like Then adjust The last row, the adjusted matrix V is represented as ： The corrected rotation matrix is: 。 6. The passive calibration method for a dynamic target according to claim 2, characterized in that, Each set of first and second spherical coordinate observation data includes spherical coordinates at at least three different times, wherein the spherical coordinates include azimuth, elevation, and distance.

7. A passive calibration method for a dynamic target according to claims 1-6, characterized in that, The dynamic target is set as a non-cooperative target that can be simultaneously locked and tracked by the phased array radar and the laser anti-drone device, including any one of drones, birds or aircraft.

8. A passive calibration system for dynamic targets, used to implement the passive calibration method for dynamic targets as described in any one of claims 1-6, characterized in that, include: The acquisition device includes radar equipment and laser anti-nuclear equipment, which are used to simultaneously track and lock onto the dynamic target and output observation data in their own coordinate system respectively. The spatiotemporal synchronization module is used to perform spatiotemporal alignment on the collected observation data and construct spatiotemporally matched observation data pairs; the data processing module receives and processes the observation data pairs, calculates the rigid body transformation parameters from the radar equipment coordinate system to the laser anti-drone equipment coordinate system, and obtains the coordinate transformation matrix for joint calibration; the control module, based on the coordinate transformation matrix for joint calibration and the current observation data, controls the laser anti-drone equipment to lock onto dynamic targets and execute strike actions.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement a passive calibration method for a dynamic target as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement a passive calibration method for a dynamic target as described in any one of claims 1-6.

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