Multi-sensor target tracking method, system and device based on consistency filtering

Abstract

This invention discloses a multi-sensor target tracking method, system, and device based on consistency filtering, relating to the field of multi-sensor target tracking. The method includes: calculating the state estimation result and information matrix for the next moment based on the state estimation results and information matrix of each sensor for the target at the previous moment; calculating an information vector based on the state estimation result and information matrix for the next moment; updating the information matrix and information vector for the next moment using the measurement information of each sensor; performing consistency iteration based on the updated information matrix and information vector to obtain a consistent iterated information matrix and information vector; and calculating the target's state vector for the next moment based on the consistent iterated information matrix and information vector, thus achieving target tracking. This invention uses a consistency iteration method to fuse the estimation results of each sensor, improving the estimation accuracy of the target by each sensor and achieving accurate target tracking.

Description

Technical Field

[0001] This invention relates to the field of multi-sensor target tracking technology, and in particular to a multi-sensor target tracking method, system and device based on consistency filtering. Background Technology

[0002] In the field of multi-sensor joint target tracking and information fusion, centralized filtering methods are commonly used for target tracking. However, this method suffers from problems such as large node information storage requirements and excessive computational burden on the processing center. To address this issue, distributed consistency filtering methods have emerged. In reality, during multi-sensor joint tracking and positioning, the sensor carriers cannot remain relatively stationary at all times; the relative positions and relative velocities between the carriers are constantly changing, resulting in inconsistent local coordinate systems for each sensor. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-sensor target tracking method, system, and device based on consistency filtering. By employing a consistency target tracking filtering method, the target tracking problem when multiple sensors are in relative motion is solved.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A multi-sensor target tracking method based on consistency filtering includes:

[0006] Based on the state estimation results of the target at the previous moment from each sensor and the information matrix at the previous moment, calculate the state estimation results and information matrix at the next moment;

[0007] Calculate the information vector based on the state estimation result and information matrix at the next time step;

[0008] The information matrix and information vector for the next moment are updated using the measurement information of the target from each sensor.

[0009] Consistency iteration is performed based on the updated information matrix and information vector to obtain the consistent iterative information matrix and information vector.

[0010] The target's state vector for the next moment is calculated based on the consistent iterative information matrix and information vector, thus enabling target tracking.

[0011] Optionally, the formulas for calculating the state estimation result and the information matrix at the next time step are as follows:

[0012]

[0013]

[0014] in, Let be the state estimation result of the target by the i-th sensor at time t. f is the state estimation result of the target by the i-th sensor at time t-1. t (·) is the state transition function at time t. Let be the information matrix of the i-th sensor at time t-1. Let W be the information matrix of the i-th sensor at time t. t-1 Let A be the information matrix of the process noise at time t-1. t Let be the Jacobian matrix of the state transition function at time t, and T denote the transpose.

[0015] Optionally, the formula for calculating the information vector is as follows:

[0016]

[0017] in, Let be the information vector of the i-th sensor at time t.

[0018] Optionally, the formula is used. Information matrix of the i-th sensor at time t Update; among them, Let be the information matrix of the i-th sensor after time t. Let V be the Jacobian matrix of the measurement function of the i-th sensor at time t. t i Let be the information matrix of the measurement noise of the i-th sensor at time t.

[0019] Optionally, the formula is used. Information vector of the i-th sensor at time t Update; among them, Let be the information vector of the i-th sensor after time t. The measurement information corrected by the i-th sensor at time t.

[0020] Optionally, the information matrix and information vector of the consistency iteration

[0021]

[0022]

[0023] in, Let π be the information vector of the i-th sensor at time t in the (l+1)th consensus iteration. i,j The consistency weight represents the weight assigned to the information vector of the j-th sensor by the i-th sensor during consistency fusion. Let be the information vector of the i-th sensor at time t during the l-th consensus iteration. Let be the information matrix of the i-th sensor at time t in the (l+1)-th consensus iteration. Let θ be the information matrix of the i-th sensor at time t during the l-th consensus iteration. i,j Let N be the projection of the state parameters of the j-th sensor relative to the i-th sensor in the local coordinate system of the i-th sensor. i Let be the set of all adjacent sensors of the i-th sensor.

[0024] Optionally, the formula for calculating the state vector of the target at the next moment is as follows:

[0025]

[0026] in, Let represent the state vector of the target acquired by the i-th sensor at time t. Let be the information matrix of the i-th sensor consistency iteration at time t. Let L be the information vector of the i-th sensor consensus iteration at time t, and L be the total number of consensus iterations.

[0027] The present invention also provides a multi-sensor target tracking system based on consistency filtering, comprising:

[0028] The next-moment state estimation result and information matrix calculation module is used to calculate the next-moment state estimation result and information matrix based on the previous-moment state estimation result and information matrix of each sensor on the target;

[0029] The information vector calculation module is used to calculate the information vector based on the state estimation result and information matrix at the next time step.

[0030] The update module is used to update the information matrix and information vector for the next moment using the measurement information of the target from each sensor.

[0031] The consistency iteration module is used to perform consistency iteration based on the updated information matrix and information vector to obtain the consistency iteration information matrix and information vector.

[0032] The state vector calculation module is used to calculate the target's state vector at the next moment based on the consistent iterative information matrix and information vector, thereby enabling target tracking.

[0033] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described multi-sensor target tracking method based on consistency filtering.

[0034] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-sensor target tracking method based on consistency filtering.

[0035] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0036] This invention employs a consensus iteration method to fuse the estimation results of each sensor, thereby improving the estimation accuracy of each sensor for the target. Furthermore, as the number of consensus iterations increases, the estimation results of each sensor tend to be consistent, achieving accurate target tracking. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a multi-sensor target tracking method based on consistency filtering provided in Embodiment 1 of the present invention;

[0039] Figure 2 This is a schematic diagram showing the relative positional relationship between multiple sensors and the target;

[0040] Figure 3 Simulation scenario for multi-sensor detection;

[0041] Figure 4 This is a schematic diagram of the X-direction position error after one consensus iteration;

[0042] Figure 5 This is a schematic diagram of the position error in the Y direction after one consensus iteration;

[0043] Figure 6 This is a schematic diagram of the velocity error in the X direction after one consensus iteration;

[0044] Figure 7 This is a schematic diagram of the velocity error in the Y direction after one consensus iteration;

[0045] Figure 8 This is a schematic diagram of the X-direction position error after 10 consensus iterations;

[0046] Figure 9 This is a schematic diagram of the position error in the Y direction after 10 consensus iterations;

[0047] Figure 10 This is a schematic diagram of the X-direction velocity error after 10 consensus iterations;

[0048] Figure 11 This is a schematic diagram of the velocity error in the Y direction after 10 consensus iterations. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The purpose of this invention is to provide a multi-sensor target tracking method, system, and device based on consistency filtering. The method uses a consistency iteration method to fuse the estimation results of each sensor, thereby improving the estimation accuracy of the target by each sensor.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Example 1

[0053] In distributed sensor networks, when performing target detection and tracking, the measurements of the target obtained by each sensor node are defined in its local coordinate system. Assume the sensor network is represented by (N, Θ), where N is the set of nodes. This represents the communication relationship between nodes. If there is a communication relationship between nodes j and i, then (i,j)∈Θ. Furthermore, for each communicable node... N i This represents the set of all its neighboring nodes. It is also assumed that all nodes are in the same time frame.

[0054] The target's state vector in the local coordinate system of sensor i can be used It means that, among them and Let represent the relative position and relative velocity of the target with respect to sensor i at time t-1, respectively. The target's motion model can then be modeled as follows:

[0055]

[0056] In the formula, f t (·) is the state transition function at time t; w t This represents noise during the state transition process; for node i, its quantity measurement of the target can be obtained by the following formula:

[0057]

[0058] In the formula, For measurement functions; Let the measurement noise of sensor i be denoted as ; assuming The noise consists of uncorrelated, zero-mean white noise with variances of [missing information]. as well as Based on the reference information filtering method, the information matrix of the noise can be defined: This is the information matrix for process noise; This is the information matrix for measuring noise.

[0059] In addition, This represents the projection of the state parameters of node j relative to node i into the local coordinate system of node i. Where (x i,j ,y i.j ,z i.j Let be the position of node j relative to node i. Let represent the velocity of node j relative to node i. Then, the target's state vector in the local coordinate system of nodes i and j is... and The following relationship can be established:

[0060]

[0061] in,

[0062]

[0063] c ij Let be the coordinate transformation matrix from the local coordinate system of node j to the local coordinate system of node i.

[0064] The above relationship can be used Figure 2 To indicate, Figure 2 middle The projection of the target's state parameters relative to node j onto the local coordinate system of node i is calculated as follows:

[0065]

[0066] This embodiment is based on Kalman filtering. Each sensor performs filtering estimation in its local coordinate system, followed by consistency estimation. Figure 1 As shown, the multi-sensor target tracking method based on consistency filtering provided in this embodiment includes the following steps:

[0067] S1: Calculate the state estimation result and information matrix of the target at the next moment based on the state estimation results of each sensor at the previous moment and the information matrix of the previous moment.

[0068] This step refers to each sensor using equations (5) and (6) to estimate the state at the previous moment. and information matrix By performing recursion, the predicted state value for the next time step is obtained. And information matrix predicted values The specific calculation method is as follows:

[0069]

[0070]

[0071] In the formula, W t-1 This is the information matrix for process noise; Let i be the information matrix of sensor i at time t-1; A is the predicted value of the information matrix of sensor i at time t; t Let Jacobian be the state transition function, and its calculation formula is:

[0072]

[0073] S2: Calculate the information vector based on the state estimation result and information matrix at the next time step.

[0074] To ensure consistency with other variables and subsequent calculations, the state vector... Find its information vector The method for obtaining it is as follows:

[0075]

[0076] S3: Update the information matrix and information vector for the next moment using the measurement information of the target from each sensor.

[0077] This step involves each sensor utilizing its own measurement information. Information matrix from the previous prediction and information vector The process of correction involves updating the obtained information matrix. and information vector This is used for subsequent consensus iteration steps. To easily distinguish the results of each iteration during the consensus iteration process, the results obtained in this step... and They are respectively denoted as and Where 0 represents the number of consistency iterations. In summary, the specific method for measurement updates is as follows:

[0078]

[0079]

[0080] In the formula, Let be the Jacobian matrix of the measurement function; The quantities to be corrected are measured; their calculation methods are as follows:

[0081]

[0082]

[0083] S4: Perform consistency iteration based on the updated information matrix and information vector to obtain the consistency iteration information matrix and information vector.

[0084] This step involves the sensors exchanging information to correct their estimation results. In a sensor network, there may be no communication link between any two sensors; therefore, information exchange between sensors is limited to their respective neighboring nodes. Assume that at time t, the information vector of sensor i in the l-th consensus iteration is... The information matrix is The calculation method for obtaining the result of the next consistency iteration is as follows:

[0085]

[0086]

[0087] In the formula, π i,j θ represents the consistency weight, indicating the proportion of information held by each neighboring node during the consistency iteration process. i,j Let be the projection of the state parameters of sensor j relative to sensor i in the local coordinate system of sensor i.

[0088] S5: Calculate the target's state vector at the next moment based on the consistent iteration information matrix and information vector to achieve target tracking.

[0089] This step involves each sensor node utilizing the consensus iteration information matrix. and information vector Inversely find the state vector The process. According to the definition of the information vector, the method for calculating the state vector is as follows:

[0090]

[0091] In the formula, and This represents the information matrix and information vector obtained after L iterations of consensus.

[0092] Thus far, the state vector at time t-1... and information matrix The state vector and information matrix at time t were obtained.

[0093] The above method is then verified through simulation:

[0094] Simulation parameters and scenarios

[0095] Set up a simulation scenario for verification. For example... Figure 3 As shown in the figure, four aircraft are used in the simulation at different speeds and along different directions to track and locate a single target. Both the target and the aircraft are moving in uniform linear motion. Other specific simulation parameters are shown in Table 1.

[0096] Table 1 Simulation parameter settings

[0097]

[0098]

[0099] Simulation results:

[0100] To verify the effectiveness of the method provided by this invention, simulations were performed using different numbers of consistency iterations. Figures 4-7 Only one consistency iteration was performed. Figures 8-11 Ten consensus iterations were performed. Comparing the results of the two methods, it can be seen that by adopting the consensus iteration method, the estimation accuracy of each sensor for the target is improved, and as the number of consensus iterations increases, the estimation results of each sensor tend to be consistent.

[0101] Example 2

[0102] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a multi-sensor target tracking system based on consistency filtering is provided below.

[0103] The system includes:

[0104] The next-moment state estimation result and information matrix calculation module is used to calculate the next-moment state estimation result and information matrix based on the previous-moment state estimation result and information matrix of each sensor on the target;

[0105] The information vector calculation module is used to calculate the information vector based on the state estimation result and information matrix at the next time step.

[0106] The update module is used to update the information matrix and information vector for the next moment using the measurement information of the target from each sensor.

[0107] The consistency iteration module is used to perform consistency iteration based on the updated information matrix and information vector to obtain the consistency iteration information matrix and information vector.

[0108] The state vector calculation module is used to calculate the target's state vector at the next moment based on the consistent iterative information matrix and information vector, thereby enabling target tracking.

[0109] Example 3

[0110] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the multi-sensor target tracking method based on consistency filtering provided in Embodiment 1.

[0111] In practical applications, the aforementioned electronic devices can be servers.

[0112] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0113] The processor, communication interface, and memory communicate with each other via a communication bus.

[0114] A communication interface is used to communicate with other devices.

[0115] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0116] Specifically, the program may include program code, which includes computer operation instructions.

[0117] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0118] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0119] Example 4

[0120] Based on the description of Embodiment 3, Embodiment 4 of the present invention provides a storage medium on which a computer program is stored. The computer program can be executed by a processor to implement the multi-sensor target tracking method based on consistency filtering of Embodiment 1.

[0121] The multi-sensor target tracking system based on consistency filtering provided in Embodiment 2 of this invention exists in various forms, including but not limited to:

[0122] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0123] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access capabilities. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0124] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0125] (4) Other electronic devices with data interaction functions.

[0126] Specific embodiments of the subject matter have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0127] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0128] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0133] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0134] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined in this invention, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0135] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] This invention can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules.

[0137] Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This invention can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0138] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0139] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-sensor target tracking method based on consistency filtering, characterized in that, include: Based on the state estimation results of the target at the previous moment from each sensor and the information matrix at the previous moment, calculate the state estimation results and information matrix at the next moment; Calculate the information vector based on the state estimation result and information matrix at the next time step; The information matrix and information vector for the next moment are updated using the measurement information of the target from each sensor. Consistency iteration is performed based on the updated information matrix and information vector to obtain the consistent iterative information matrix and information vector. The target's state vector for the next moment is calculated based on the consistent iteration information matrix and information vector, thus achieving target tracking; The formulas for calculating the information matrix and information vector in the consistency iteration are as follows: in, Let the i-th sensor at time t be at the i-th position. l Information vector from +1 consecutive iterations of consistency The consistency weight represents the weight assigned to the information vector of the j-th sensor by the i-th sensor during consistency fusion. Let the i-th sensor at time t be at the i-th position. l The information vector of the next consistency iteration. Let the i-th sensor at time t be at the i-th position. l Information matrix after +1 consistency iterations, Let the i-th sensor at time t be at the i-th position. l Information matrix of the next consistency iteration Let be the projection of the state parameters of the j-th sensor relative to the i-th sensor in the local coordinate system of the i-th sensor. Let be the set of all adjacent sensors of the i-th sensor.

2. The multi-sensor target tracking method based on consistency filtering according to claim 1, characterized in that, The formulas for calculating the state estimation result and the information matrix at the next time step are as follows: in, Let be the state estimation result of the target by the i-th sensor at time t. This represents the state estimation result of the target by the i-th sensor at time t-1. Let be the state transition function at time t. Let be the information matrix of the i-th sensor at time t-1. Let be the information matrix of the i-th sensor at time t. This is the information matrix of the process noise at time t-1. Let be the Jacobian matrix of the state transition function at time t, and T denote the transpose.

3. The multi-sensor target tracking method based on consistency filtering according to claim 2, characterized in that, The formula for calculating the information vector is as follows: in, Let be the information vector of the i-th sensor at time t.

4. The multi-sensor target tracking method based on consistency filtering according to claim 3, characterized in that, Using formula Information matrix of the i-th sensor at time t Update; among them, Let be the information matrix of the i-th sensor after time t. Let be the Jacobian matrix of the measurement function of the i-th sensor at time t. Let be the information matrix of the measurement noise of the i-th sensor at time t.

5. The multi-sensor target tracking method based on consistency filtering according to claim 4, characterized in that, Using formula Information vector of the i-th sensor at time t Update; among them, Let be the information vector of the i-th sensor after time t. The measurement information corrected by the i-th sensor at time t.

6. The multi-sensor target tracking method based on consistency filtering according to claim 5, characterized in that, The formula for calculating the target's state vector at the next moment is as follows: in, Let represent the state vector of the target acquired by the i-th sensor at time t. Let be the information matrix of the i-th sensor consistency iteration at time t. Let L be the information vector of the i-th sensor consensus iteration at time t, and L be the total number of consensus iterations.

7. A multi-sensor target tracking system based on consistency filtering, characterized in that, include: The next-moment state estimation result and information matrix calculation module is used to calculate the next-moment state estimation result and information matrix based on the previous-moment state estimation result and information matrix of each sensor on the target; The information vector calculation module is used to calculate the information vector based on the state estimation result and information matrix at the next time step. The update module is used to update the information matrix and information vector for the next moment using the measurement information of the target from each sensor. The consistency iteration module is used to perform consistency iteration based on the updated information matrix and information vector to obtain the consistency iteration information matrix and information vector. The state vector calculation module is used to calculate the target's state vector at the next moment based on the consistent iteration information matrix and information vector, thereby enabling target tracking. The formulas for calculating the information matrix and information vector in the consistency iteration are as follows: in, Let the i-th sensor at time t be at the i-th position. l Information vector from +1 consecutive iterations of consistency The consistency weight represents the weight assigned to the information vector of the j-th sensor by the i-th sensor during consistency fusion. Let the i-th sensor at time t be at the i-th position. l The information vector of the next consistency iteration. Let the i-th sensor at time t be at the i-th position. l Information matrix after +1 consistency iterations, Let the i-th sensor at time t be at the i-th position. l Information matrix of the next consistency iteration Let be the projection of the state parameters of the j-th sensor relative to the i-th sensor in the local coordinate system of the i-th sensor. Let be the set of all adjacent sensors of the i-th sensor.

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the multi-sensor target tracking method based on consistency filtering as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the multi-sensor target tracking method based on consistency filtering as described in any one of claims 1-6.

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