A high-speed aircraft clustering method and device for collaborative sensing and positioning

Abstract

Embodiments of the present invention provide a high-speed aircraft clustering method and device for collaborative sensing and positioning. The method includes: using a TOA-based collaborative sensing and positioning algorithm to perform collaborative sensing and positioning of non-cooperative targets using high-speed aircraft in a high-speed aircraft cluster as anchor nodes; introducing two positioning error terms during collaborative sensing and positioning: a distance measurement error related to the distance from the high-speed aircraft anchor node to the non-cooperative target, and a speed error related to the speed of the high-speed aircraft anchor node; calculating a comprehensive error covariance matrix in a Euclidean coordinate system based on the distance measurement error and the speed error; and constructing a high-speed aircraft anchor node clustering model based on the comprehensive error covariance matrix; and optimizing and solving the high-speed aircraft anchor node clustering model to obtain the optimal high-speed aircraft anchor node clustering result. In this way, high-speed aircraft clustering can be achieved in actual confrontation scenarios, effectively improving the clustering effect.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed aircraft clustering, and in particular to a high-speed aircraft clustering method and device for collaborative sensing and positioning. Background Art

[0002] Utilizing swarms of high-speed aircraft to collaboratively perceive non-cooperative targets in the environment is a crucial task in adversarial scenarios. Currently, high-speed aircraft swarms have become an emerging force in adversarial scenarios. Collaborative localization is a crucial component of collaborative perception and a key topic in navigation and signal processing. Given the limited resources, insufficient individual perception capabilities, and widespread node distribution of swarms, it is necessary to construct a clustered perception and localization model for wide-area collaborative perception. Furthermore, algorithms are needed to design optimal clustered localization schemes for potential non-cooperative targets in the environment, improve the accuracy of collaborative localization, and enhance the real-time perception capabilities and reliability of the swarm.

[0003] High-speed aircraft clustering for collaborative perception and positioning refers to the use of high-speed aircraft to perceive and locate several potential non-cooperative targets in a three-dimensional environment. The high-speed aircraft are divided into several clusters, and collaborative perception and positioning of non-cooperative targets are performed on a cluster basis to optimize the positioning effect.

[0004] At present, a large number of works have been carried out to study non-cooperative target localization and wireless sensor clustering respectively, but almost no work focuses on clustering and non-cooperative target localization of high-speed aircraft at the same time, and even fewer works consider high-speed aircraft in actual confrontation scenarios. Summary of the Invention

[0005] In a first aspect, an embodiment of the present invention provides a high-speed aircraft clustering method for collaborative sensing and positioning, the method comprising:

[0006] Using the TOA-based cooperative sensing and positioning algorithm, high-speed aircraft in the high-speed aircraft cluster are used as anchor nodes to perform cooperative sensing and positioning of non-cooperative targets.

[0007] In collaborative sensing and positioning, two positioning error terms are introduced: the distance measurement error related to the distance between the high-speed aircraft anchor node and the non-cooperative target, and the speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix.

[0008] The high-speed aircraft anchor node clustering model is optimized and solved to obtain the optimal high-speed aircraft anchor node clustering result.

[0009] In some implementations of the first aspect, calculating a comprehensive error covariance matrix in a Euclidean coordinate system based on the distance measurement error and the speed error includes:

[0010] Calculate the covariance matrix of distance measurement error and velocity error in the Euclidean coordinate system;

[0011] The covariance matrix of the distance measurement error and the velocity error in the Euclidean coordinate system is added together to obtain the comprehensive error covariance matrix in the Euclidean coordinate system.

[0012] In some implementations of the first aspect, the covariance matrix of the distance measurement error in the Euclidean coordinate system is calculated using the following formula:

[0013]

[0014] in, represents the covariance matrix of the distance measurement error in the Euclidean coordinate system; k represents the scale factor; r m,n represents the distance from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m; u m,n represents the unit vector pointing from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m.

[0015] In some implementations of the first aspect, the covariance matrix of the velocity error in the Euclidean coordinate system is calculated using the following formula:

[0016]

[0017] in, represents the covariance matrix of velocity error in the Euclidean coordinate system; t represents the proportional coefficient; v n represents the speed of the high-speed aircraft anchor node n; d n Represents the velocity direction unit vector at the position of the high-speed aircraft anchor node n.

[0018] In some implementations of the first aspect, constructing a high-speed aircraft anchor node clustering model based on a comprehensive error covariance matrix includes:

[0019] According to the comprehensive error covariance matrix of each high-speed aircraft anchor node in the high-speed aircraft anchor node cluster relative to the non-cooperative targets that the high-speed aircraft anchor node cluster is responsible for collaborative perception and positioning, the Cramer-Rao bound corresponding to the high-speed aircraft anchor node cluster is calculated, and based on this, a high-speed aircraft anchor node clustering model is constructed.

[0020] In some implementations of the first aspect, optimizing and solving the high-speed aircraft anchor node clustering model to obtain an optimal high-speed aircraft anchor node clustering result includes:

[0021] The high-speed aircraft anchor node clustering model is converted into a generalized assignment problem model;

[0022] The Gurobi algorithm in the YALMIP solving tool is used to optimize and solve the generalized assignment problem model, and the optimal high-speed aircraft anchor node clustering result is obtained.

[0023] In some implementations of the first aspect, after optimizing and solving the high-speed aircraft anchor node clustering model to obtain an optimal high-speed aircraft anchor node clustering result, the method further includes:

[0024] The high-speed aircraft anchor node cluster corresponding to the non-cooperative target is used to perform collaborative perception and positioning of the non-cooperative target.

[0025] In a second aspect, an embodiment of the present invention provides a high-speed aircraft clustering device for collaborative sensing and positioning, the device comprising:

[0026] The positioning module is used to use a TOA-based collaborative sensing positioning algorithm to perform collaborative sensing positioning on non-cooperative targets using high-speed aircraft in the high-speed aircraft cluster as anchor nodes;

[0027] A construction module is used to introduce two positioning error terms during collaborative sensing and positioning: a distance measurement error related to the distance from the high-speed aircraft anchor node to the non-cooperative target, and a speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix.

[0028] The solution module is used to optimize and solve the high-speed aircraft anchor node clustering model to obtain the optimal high-speed aircraft anchor node clustering result.

[0029] In a third aspect, an embodiment of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described above.

[0030] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the method described above.

[0031] According to the embodiments of the present invention, at least the following technical effects are achieved:

[0032] This paper proposes a high-speed aircraft clustering method for collaborative sensing and positioning scenarios involving high-speed aircraft. This method is applicable to collaborative sensing scenarios in three-dimensional space and specifically addresses clustering errors caused by the high speed of high-speed aircraft anchor nodes in actual adversarial scenarios. Furthermore, the Gurobi method achieves high accuracy in solving the generalized assignment problem model. Therefore, this paper possesses strong practical value and transferability.

[0033] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0035] Figure 1 A flowchart of a high-speed aircraft clustering method for collaborative sensing and positioning provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of the superposition of two positioning errors provided by an embodiment of the present invention;

[0037] Figure 3 CRLB contour map of each position in space provided by an embodiment of the present invention;

[0038] Figure 4 A schematic diagram of a clustering result of high-speed aircraft anchor nodes provided by an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of another high-speed aircraft anchor node clustering result provided by an embodiment of the present invention;

[0040] Figure 6 A structural diagram of a high-speed aircraft clustering device for collaborative sensing and positioning provided by an embodiment of the present invention;

[0041] Figure 7 is a structural diagram of an exemplary electronic device capable of implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0044] In response to the problems arising from the background technology, the embodiments of the present invention provide a high-speed aircraft clustering method, apparatus, device and storage medium for collaborative sensing and positioning. Specifically, a TOA-based collaborative sensing and positioning algorithm is used, and the high-speed aircraft in the high-speed aircraft cluster are used as anchor nodes to perform collaborative sensing and positioning on non-cooperative targets. During collaborative sensing and positioning, two positioning error terms are introduced, namely, the distance measurement error related to the distance from the high-speed aircraft anchor node to the non-cooperative target, and the speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix. The high-speed aircraft anchor node clustering model is optimized and solved to obtain the optimal high-speed aircraft anchor node clustering result. In this way, high-speed aircraft clustering in actual confrontation scenarios can be achieved, and the clustering effect can be effectively improved.

[0045] In the following, with reference to the accompanying drawings, a high-speed aircraft clustering method, apparatus, device, and storage medium for collaborative sensing and positioning provided by the embodiments of the present invention will be described in detail through specific embodiments.

[0046] Figure 1 A flowchart of a high-speed aircraft clustering method for collaborative sensing and positioning provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the high-speed aircraft clustering method 100 may include the following steps:

[0047] S110 , using a TOA-based collaborative sensing and positioning algorithm, with high-speed aircraft in the high-speed aircraft cluster as anchor nodes, to perform collaborative sensing and positioning on non-cooperative targets.

[0048] S120, during collaborative perception positioning, two positioning error terms are introduced, namely the distance measurement error related to the distance from the high-speed aircraft anchor node to the non-cooperative target, and the speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix.

[0049] In some embodiments, the covariance matrix of the distance measurement error and the speed error in the Euclidean coordinate system may be calculated, and the covariance matrices of the distance measurement error and the speed error in the Euclidean coordinate system may be added together to obtain a comprehensive error covariance matrix in the Euclidean coordinate system.

[0050] For example, the covariance matrix of the distance measurement error in the Euclidean coordinate system can be calculated by the following formula:

[0051]

[0052] in, represents the covariance matrix of the distance measurement error in the Euclidean coordinate system; k represents the scale factor; r m,n represents the distance from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m; u m,n represents the unit vector pointing from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m.

[0053] The covariance matrix of the velocity error in the Euclidean coordinate system can be calculated by the following formula:

[0054]

[0055] in, represents the covariance matrix of velocity error in the Euclidean coordinate system; t represents the proportional coefficient; v n represents the speed of the high-speed aircraft anchor node n; d n Represents the velocity direction unit vector at the position of the high-speed aircraft anchor node n.

[0056] In some embodiments, the Cramer-Rao bound (CRLB) corresponding to the high-speed aircraft anchor node cluster can be calculated based on the comprehensive error covariance matrix of each high-speed aircraft anchor node in the high-speed aircraft anchor node cluster relative to the non-cooperative target that the high-speed aircraft anchor node cluster is responsible for collaborative perception and positioning, and a high-speed aircraft anchor node clustering model can be constructed based on this.

[0057] S130 , optimizing and solving the high-speed aircraft anchor node clustering model to obtain an optimal high-speed aircraft anchor node clustering result.

[0058] In some embodiments, the high-speed aircraft anchor node clustering model can be converted into a generalized assignment problem model, and the generalized assignment problem model can be optimized and solved using the Gurobi algorithm in the YALMIP solver to obtain the optimal high-speed aircraft anchor node clustering result. After this, the high-speed aircraft anchor node cluster corresponding to the non-cooperative target can be used to perform collaborative sensing and positioning on the non-cooperative target.

[0059] To facilitate further understanding, the above content is described below in conjunction with specific embodiments:

[0060] Assume that there are N high-speed aircraft as anchor nodes in the high-speed aircraft cluster, represented by the set N = {1, 2, ..., n, ... N}, and the position of the high-speed aircraft anchor node n is represented by p n ; There are M non-cooperative targets in the space, using the set Indicates that the non-cooperative target is the target to be perceived and located by the high-speed aircraft cluster. The location is uncertain for the high-speed aircraft cluster, but the high-speed aircraft cluster has a priori approximate location of it, which is expressed as q m .

[0061] Considering the use of TOA-based collaborative sensing positioning, p n to q m The target distance is r m,n =‖p n ―q m However, in actual measurement, due to the normal distribution error of the time delay received by the sensor, the measured distance is accompanied by an error term, which can be obtained Where e is the noise. Since the distance measurement error from the non-cooperative target to the anchor nodes of different high-speed aircraft varies with the distance, the error has a mean of 0 and a variance of σ 2 =k(r m,n ) 4 The normal distribution of , k represents the proportional coefficient.

[0062] Transforming the distance measurement error into the Euclidean coordinate system, the observation of the real position of the non-cooperative target m by the high-speed aircraft anchor node n can be expressed as represents the equivalent form of e in the equivalent Euclidean coordinate system, represents the multivariate normal distribution, Represents the covariance matrix, which is represented by Calculate, where u m,n Indicates that from p n Point to q m The unit vector of

[0063] On this basis, consider the speed of the high-speed aircraft anchor node n is v nSince the high-speed aircraft anchor node n is in motion, it needs to measure its own position through external sensors (such as GPS devices, infrared sensors, etc.). Therefore, its measurement of its own position is not real-time. Assuming that the high-speed aircraft anchor node n locates itself with a clock of constant frequency, its positioning error is proportional to its own speed ‖v n ‖ is positively correlated, and this positioning error will affect the positioning of non-cooperative targets. When the high-speed aircraft anchor node n is in high-speed motion, the positioning error of non-cooperative targets It can be expressed as a multivariate normal distribution in the Euclidean coordinate system, using It means that the covariance matrix Among them, d n Indicates p n The velocity direction unit vector at t represents the proportionality coefficient.

[0064] The above two positioning errors both obey the multivariate normal distribution in the Euclidean coordinate system. According to the basic properties of the multivariate normal distribution, the sum of their errors is It also obeys the multivariate normal distribution with a mean of 0, and the covariance matrix is ​​the linear sum of two terms, that is, like Figure 2 As shown here, the superposition effect of the two positioning errors is shown. What needs to be understood is that Figure 2 Two dimensions are used for illustration, but in actual applications, it is applicable to both two-dimensional and three-dimensional scenes.

[0065] The present invention uses CRLB to measure the positioning accuracy of a certain location, wherein the specific calculation process of CRLB is as follows:

[0066] Compute the conditional probability density function:

[0067]

[0068] Taking the logarithm on both sides of the above equation, we get the log-likelihood function:

[0069]

[0070] Assume there is a high-speed aircraft anchor node cluster with a high-speed aircraft anchor node At the same time, the position of the non-cooperative target m is observed. The joint conditional probability density function of the independent distance estimators obtained by measurement is:

[0071]

[0072] Therefore, the log joint likelihood function is:

[0073]

[0074] In 3D space, the Fisher information matrix can be expressed as:

[0075]

[0076] According to the definition of Fisher matrix, the elements of the matrix can be solved as follows:

[0077]

[0078] Among them, θ1 and θ2 are parameters, which are two of x, y, and z. Using the properties of the multivariate normal distribution, we can calculate the above expected value, and thus calculate that the terms of the Fisher matrix are exactly the terms of the inverse of its covariance matrix, and the calculated Fisher matrix is:

[0079]

[0080] In particular, when Σ m,n When it is not invertible, the above inverse matrix is ​​replaced by its Moore-Penrose inverse. According to the definition of Fisher matrix, its existence does not depend on Σ m,n The reversibility of Σ is not difficult to prove. m,n When irreversible, Σ m,n The Moore-Penrose inverse of gives the same results as the definition method for solving the Fisher matrix. Therefore, for simplicity, this paper uniformly uses (·) ―1 It represents the inverse of an invertible matrix or the Moore-Penrose inverse of an irreversible matrix, which does not affect the estimated amount of positioning error. The CRLB is calculated as:

[0081]

[0082] Where tr(·) represents the trace of the matrix. Using three high-speed aircraft anchor nodes to locate each position in space, its CRLB can be as follows: Figure 3 As shown. It is important to understand that Figure 3 Two dimensions are used for illustration, but in actual applications, it is applicable to two-dimensional and three-dimensional scenes; three high-speed aircraft anchor nodes are used for illustration, but in actual applications, it is applicable to any number of high-speed aircraft anchor nodes.

[0083] Combining the above process, we get the high-speed aircraft anchor node clustering model. Specifically, the high-speed aircraft anchor nodes are divided into M clusters for M non-cooperative targets. It represents the high-speed aircraft anchor node cluster for the non-cooperative target m, and its CRLB for the non-cooperative target m can be calculated.

[0084] In order to find the best clustering solution, the following optimization problem model is established, where f is the objective function and st is the constraint condition

[0085]

[0086] x m,n ∈{0,1};

[0087] Among them, x m,n It is a 0-1 variable, which is used to indicate whether the high-speed aircraft anchor node n perceives and locates the non-cooperative target m. Then x m,n =1, otherwise x m,n = 0. Due to the TOA positioning principle in three-dimensional space, at least four high-speed aircraft anchor nodes are required to locate each non-cooperative target. It can be seen that the high-speed aircraft anchor node clustering model is converted into a generalized assignment problem model.

[0088] For the generalized assignment problem model, the Gurobi algorithm in the YALMIP solving tool is used to optimize the solution and obtain the optimal high-speed aircraft anchor node clustering result.

[0089] The following shows the clustering solution results for two scenarios: medium-scale (10 non-cooperative targets, 100 high-speed aircraft anchor nodes) and large-scale (40 non-cooperative targets, 200 high-speed aircraft anchor nodes), as shown in Figure 2. Figure 4 、 Figure 5 The optimal value of the objective function for the medium-scale solution is 9.5526. For the 10 non-cooperative targets, there are [10, 9, 10, 6, 7, 10, 7, 16, 13, 12] high-speed aircraft anchor nodes for collaborative sensing and positioning. The optimal value of the objective function for the large-scale solution is 406.0018. Due to space limitations, the specific solution results are not shown here.

[0090] The optimal solution obtained by the Gurobi algorithm is relatively stable in multiple solutions. Compared with other algorithms, the Gurobi algorithm has a significant advantage in solution time, as shown in Table 1, and has strong solution accuracy for this generalized assignment problem model.

[0091] Table 1

[0092] Solving algorithm / scale Gurobi BNB INTLINPROG CUTSDP medium-sized 0.0295s 0.1425s 0.0879s 0.0523s Larger scale 0.0322s 0.3918s 0.0614s 0.1245s

[0093] It can be seen that the present invention has at least the following advantages:

[0094] 1. Applicable to scenarios where multiple high-speed aircraft anchor nodes collaboratively perceive and locate multiple non-cooperative targets.

[0095] 2. The two error terms of the distance error of the non-cooperative target and the speed error of the high-speed aircraft anchor node are considered in the positioning model, which is more in line with the actual situation.

[0096] 3. Use the Gurobi method to solve the generalized assignment problem, optimize the sum of the Cramer-Rao bounds of each cluster node, and solve a better clustering solution.

[0097] Accordingly, the present invention achieves at least the following technical effects:

[0098] This paper proposes a high-speed aircraft clustering method for collaborative sensing and positioning scenarios involving high-speed aircraft. This method is applicable to collaborative sensing scenarios in three-dimensional space and specifically addresses clustering errors caused by the high speed of high-speed aircraft anchor nodes in actual adversarial scenarios. Furthermore, the Gurobi method achieves high accuracy in solving the generalized assignment problem model. Therefore, this paper possesses strong practical value and transferability.

[0099] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0100] The above is an introduction to a method embodiment. The following further illustrates the solution of the present invention through an apparatus embodiment.

[0101] Figure 6 The structure diagram of a high-speed aircraft clustering device for cooperative sensing and positioning provided by an embodiment of the present invention is as follows: Figure 6 As shown, the high-speed aircraft clustering device 600 may include:

[0102] The positioning module 610 is configured to use a TOA-based collaborative sensing positioning algorithm to perform collaborative sensing positioning on non-cooperative targets using high-speed aircraft in the high-speed aircraft cluster as anchor nodes;

[0103] Construction module 620 is used to introduce two positioning error terms during cooperative sensing positioning: a distance measurement error related to the distance between the high-speed aircraft anchor node and the non-cooperative target, and a speed error related to the speed of the high-speed aircraft anchor node; calculate a comprehensive error covariance matrix in a Euclidean coordinate system based on the distance measurement error and the speed error; and construct a high-speed aircraft anchor node clustering model based on the comprehensive error covariance matrix;

[0104] The solving module 630 is used to optimize and solve the high-speed aircraft anchor node clustering model to obtain the optimal high-speed aircraft anchor node clustering result.

[0105] It is understandable that Figure 6 Each module / unit in the high-speed aircraft clustering device 600 has the function of realizing Figure 1 The functions of the various steps in the high-speed aircraft clustering method 100 shown and their ability to achieve corresponding technical effects are not described here for the sake of brevity.

[0106] Figure 7 7 is a block diagram of an exemplary electronic device capable of implementing embodiments of the present invention. Electronic device 700 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 700 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided for example only and are not intended to limit the implementation of the invention described and / or claimed herein.

[0107] like Figure 7 As shown, the electronic device 700 may include a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 may also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0108] Multiple components in the electronic device 700 are connected to the I / O interface 705, including an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0109] The computing unit 701 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 can be implemented as a computer program product, including a computer program tangibly embodied in a computer-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform method 100 in any other appropriate manner (e.g., by means of firmware).

[0110] The various embodiments described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of the present invention, computer-readable media can be tangible media that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable media can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of computer-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0113] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effects achieved by executing the method in an embodiment of the present invention. For the sake of brevity, they will not be repeated here.

[0114] In addition, the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method 100 is implemented.

[0115] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. The present invention is not limited here.

[0116] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A high-speed aircraft clustering method for collaborative sensing and positioning, characterized in that: The method comprises: Using the TOA-based cooperative sensing and positioning algorithm, high-speed aircraft in the high-speed aircraft cluster are used as anchor nodes to perform cooperative sensing and positioning of non-cooperative targets. In collaborative sensing and positioning, two positioning error terms are introduced: the distance measurement error related to the distance between the high-speed aircraft anchor node and the non-cooperative target, and the speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix. The high-speed aircraft anchor node clustering model is optimized and solved to obtain the optimal high-speed aircraft anchor node clustering result; Calculating the comprehensive error covariance matrix in the Euclidean coordinate system based on the distance measurement error and the speed error includes: Calculate the covariance matrix of distance measurement error and velocity error in the Euclidean coordinate system; Add the covariance matrices of the distance measurement error and the velocity error in the Euclidean coordinate system to obtain the comprehensive error covariance matrix in the Euclidean coordinate system; The method of constructing a high-speed aircraft anchor node clustering model based on a comprehensive error covariance matrix includes: Based on the comprehensive error covariance matrix of each high-speed aircraft anchor node in the high-speed aircraft anchor node cluster relative to the non-cooperative target that the high-speed aircraft anchor node cluster is responsible for collaborative perception and positioning, the Cramer-Rao bound corresponding to the high-speed aircraft anchor node cluster is calculated, and based on this, a high-speed aircraft anchor node clustering model is constructed; The optimizing and solving the high-speed aircraft anchor node clustering model to obtain the optimal high-speed aircraft anchor node clustering result includes: The high-speed aircraft anchor node clustering model is converted into a generalized assignment problem model; The Gurobi algorithm in the YALMIP solving tool is used to optimize and solve the generalized assignment problem model, and the optimal high-speed aircraft anchor node clustering result is obtained.

2. The method according to claim 1, characterized in that The covariance matrix of the distance measurement error in the Euclidean coordinate system is calculated by the following formula: in, represents the covariance matrix of the distance measurement error in the Euclidean coordinate system; k represents the scale factor; r m,n represents the distance from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m; u m,n represents the unit vector pointing from the position of the high-speed aircraft anchor node n to the prior position of the non-cooperative target m.

3. The method according to claim 1, characterized in that The covariance matrix of the velocity error in the Euclidean coordinate system is calculated by the following formula: in, represents the covariance matrix of velocity error in the Euclidean coordinate system; t represents the proportional coefficient; v n represents the speed of the high-speed aircraft anchor node n; d n Represents the velocity direction unit vector at the position of the high-speed aircraft anchor node n.

4. The method according to any one of claims 1 to 3, characterized in that After optimizing and solving the high-speed aircraft anchor node clustering model to obtain an optimal high-speed aircraft anchor node clustering result, the method further includes: The high-speed aircraft anchor node cluster corresponding to the non-cooperative target is used to perform collaborative perception and positioning of the non-cooperative target.

5. A high-speed aircraft clustering device for collaborative sensing and positioning, characterized in that: The device comprises: The positioning module is used to use a TOA-based collaborative sensing positioning algorithm to perform collaborative sensing positioning on non-cooperative targets using high-speed aircraft in the high-speed aircraft cluster as anchor nodes; A construction module is used to introduce two positioning error terms during collaborative sensing and positioning: a distance measurement error related to the distance from the high-speed aircraft anchor node to the non-cooperative target, and a speed error related to the speed of the high-speed aircraft anchor node. The comprehensive error covariance matrix in the Euclidean coordinate system is calculated based on the distance measurement error and the speed error, and a high-speed aircraft anchor node clustering model is constructed based on the comprehensive error covariance matrix. A solution module is used to optimize and solve the high-speed aircraft anchor node clustering model to obtain the optimal high-speed aircraft anchor node clustering result; The building blocks are specifically used for: Calculate the covariance matrix of the distance measurement error and the velocity error in the Euclidean coordinate system; add the covariance matrices of the distance measurement error and the velocity error in the Euclidean coordinate system to obtain the comprehensive error covariance matrix in the Euclidean coordinate system; Based on the comprehensive error covariance matrix of each high-speed aircraft anchor node in the high-speed aircraft anchor node cluster relative to the non-cooperative target that the high-speed aircraft anchor node cluster is responsible for collaborative perception and positioning, the Cramer-Rao bound corresponding to the high-speed aircraft anchor node cluster is calculated, and based on this, a high-speed aircraft anchor node clustering model is constructed; The solution module is specifically used for: The high-speed aircraft anchor node clustering model is converted into a generalized assignment problem model; The Gurobi algorithm in the YALMIP solving tool is used to optimize and solve the generalized assignment problem model, and the optimal high-speed aircraft anchor node clustering result is obtained.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 4.

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