High-speed aircraft clustering method and device for cooperative sensing positioning

Abstract

The embodiment of the invention provides a high-speed aircraft clustering method and device for cooperative sensing positioning. The method comprises the following steps: using a TOA-based collaborative perception positioning algorithm, and taking a high-speed aircraft in a high-speed aircraft cluster as an anchor node to carry out collaborative perception positioning on a non-cooperative target; during cooperative sensing positioning, two positioning error items are introduced, namely a distance measurement error related to the distance from a high-speed aircraft anchor node to a non-cooperative target and a speed error related to the speed of the high-speed aircraft anchor node, and a comprehensive error covariance matrix under a European-style coordinate system is calculated according to the distance measurement error and the speed error; building a high-speed aircraft anchor node clustering model based on the comprehensive error covariance matrix; and carrying out optimization solution on the high-speed aircraft anchor node clustering model to obtain an optimal high-speed aircraft anchor node clustering result. In this way, high-speed aircraft clustering in an actual confrontation scene can be achieved, and the clustering effect is effectively improved.

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] Using high-speed aircraft clusters to collaboratively perceive non-cooperative targets in the environment is an important task in confrontation scenarios. At present, high-speed flight clusters have become an emerging force in confrontation scenarios. Collaborative positioning is an important link in collaborative perception and an important topic in the field of navigation and signal processing. In view of the characteristics of limited cluster resources, insufficient single-unit perception, and wide distribution of nodes, it is necessary to build a cluster perception and positioning model for cluster wide-area collaborative perception. It is also necessary to use algorithms to design the best cluster positioning solution for potential non-cooperative targets in the environment, improve the collaborative perception and positioning accuracy of the cluster, and enhance the real-time perception capability and reliability of the cluster.

[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, in which 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 positioning and wireless sensor clustering respectively, but almost no work focuses on clustering and non-cooperative target positioning 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 positioning, the method comprising:

[0006] Using the TOA-based collaborative sensing and positioning algorithm, the high-speed aircraft in the high-speed aircraft cluster is used as the anchor node to perform collaborative sensing and positioning on non-cooperative targets.

[0007] In collaborative sensing 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.

[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 according to a distance measurement error and a speed error includes:

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

[0011] The covariance matrices of the distance measurement error and the velocity error in the Euclidean coordinate system are 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 by the following formula:

[0013]

[0014] in, represents the covariance matrix of distance measurement error in the Euclidean coordinate system; k represents the proportionality coefficient; 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 by 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 unit vector of the velocity direction 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 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.

[0020] In some implementable manners 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 transformed 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] A 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 a high-speed aircraft cluster as anchor nodes;

[0027] A construction module is used to introduce two positioning error terms during collaborative sensing positioning, namely, 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 so that the at least one processor can 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] The present invention proposes a high-speed aircraft clustering method for the cooperative sensing and positioning scenario of high-speed aircraft, which is applicable to the cooperative sensing scenario in three-dimensional space, and focuses on the clustering error caused by the high speed of the high-speed aircraft anchor node in the actual confrontation scenario. In addition, the accuracy of solving the generalized assignment problem model using the Gurobi method is relatively high. Therefore, the present invention has 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 easily 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, wherein:

[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 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 Another schematic diagram of high-speed aircraft anchor node clustering results 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] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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 creative work are within the scope of protection of the present invention.

[0043] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0044] In response to the problems arising in the background technology, the embodiments of the present invention provide a high-speed aircraft clustering method, device, equipment and storage medium for collaborative sensing and positioning. Specifically, a TOA-based collaborative sensing and positioning algorithm is used to perform collaborative sensing and positioning of non-cooperative targets with high-speed aircraft in a high-speed aircraft cluster as anchor nodes; 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 conjunction with the accompanying drawings, a high-speed aircraft clustering method, device, equipment and storage medium for collaborative sensing and positioning provided by an embodiment 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 positioning algorithm, with high-speed aircraft in the high-speed aircraft cluster as anchor nodes, to perform collaborative sensing positioning on non-cooperative targets.

[0048] S120, in collaborative sensing 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 distance measurement error in the Euclidean coordinate system; k represents the proportionality coefficient; 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 unit vector of the velocity direction 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 that, the high-speed aircraft anchor node cluster corresponding to the non-cooperative target can be used to perform collaborative perception and positioning on the non-cooperative target.

[0059] In order to facilitate further understanding, the above content is described in detail 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 sensed and located by the high-speed aircraft cluster. The position is uncertain for the high-speed aircraft cluster, but the high-speed aircraft cluster has a priori approximate position 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, since the time delay received by the sensor has a normally distributed error, the measured distance has an error term, which can be obtained Where e is noise. Since the distance measurement error from the non-cooperative target to different high-speed aircraft anchor nodes 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 a multivariate normal distribution, represents the covariance matrix, which is 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 as 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.), so 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. 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 are subject to 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 two positioning errors is shown. It should be understood that Figure 2 Two dimensions are used for illustration, but in actual applications, the method 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 of 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 high-speed aircraft anchor nodes At the same time, the position of the non-cooperative target m is observed. Then, for the independent distance estimates obtained by measurement, the joint conditional probability density function 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, θ 1 ,θ 2 is the parameter, and is two of x, y, and z. Using the properties of the multivariate normal distribution, the above expected value can be calculated, and thus the terms of the Fisher matrix are calculated to be the inverse of its covariance matrix. The Fisher matrix is ​​calculated as:

[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 m,n When irreversible, Σ m,n The Moore-Penrose inverse of gives the same result as the definition method for solving the Fisher matrix. Therefore, for the sake of simplicity, this paper uniformly uses (·) ―1 represents the inverse of a reversible matrix or the Moore-Penrose inverse of an irreversible matrix, which does not affect the estimated amount of positioning error. 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 clustering model of high-speed aircraft anchor nodes. 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 solve the best clustering solution, the following optimization problem model is established here, 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 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, for each non-cooperative target, at least four high-speed aircraft anchor nodes are required to locate it. 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 cases: 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 below. Figure 4 , Figure 5 As shown in the figure, the optimal value of the objective function is 9.5526 for medium-scale solution. For 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 is 406.0018 for large-scale solution. Due to space limitations, the specific solution results are not shown.

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

[0091] Table 1

[0092] Solving algorithm / scale Gurobi BNB INTLINPROG CUTSDP Medium size 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 the scenario of collaborative perception and positioning of multiple non-cooperative targets by multiple high-speed aircraft anchor nodes.

[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] The present invention proposes a high-speed aircraft clustering method for the cooperative sensing and positioning scenario of high-speed aircraft, which is applicable to the cooperative sensing scenario in three-dimensional space, and focuses on the clustering error caused by the high speed of the high-speed aircraft anchor node in the actual confrontation scenario. In addition, the Gurobi method is used to solve the generalized assignment problem model with high accuracy. Therefore, the present invention has strong practical value and transferability.

[0099] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the 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 is a further explanation of the solution of the present invention through an apparatus embodiment.

[0101] 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, such as Figure 6 As shown, the high-speed aircraft clustering device 600 may include:

[0102] The positioning module 610 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;

[0103] A construction module 620 is used to introduce two positioning error terms during cooperative sensing positioning, namely, 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, calculate a comprehensive error covariance matrix in a Euclidean coordinate system according to 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 solution 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] Understandably, 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 the ability to achieve the corresponding technical effects are not described in detail here for the sake of brevity.

[0106] Figure 7 700 is a block diagram of an exemplary electronic device capable of implementing an embodiment 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[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. In the RAM 703, various programs and data required for the operation of the electronic device 700 may also be stored. 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 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 through a computer network such as the Internet and / or various telecommunication networks.

[0109] The computing unit 701 may be a variety of general and / or special 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program, which is tangibly contained in a computer-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via ROM 702 and / or a 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 may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the 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), load 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, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may 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. These program codes 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 the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of the present invention, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, 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 effect achieved by the embodiments of the present invention executing the method. For the sake of concise description, they are not 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 processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope 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 collaborative sensing and positioning algorithm, the high-speed aircraft in the high-speed aircraft cluster is used as the anchor node to perform collaborative sensing and positioning on non-cooperative targets. In collaborative sensing 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.

2. The method according to claim 1, characterized in that The method of calculating the comprehensive error covariance matrix in the Euclidean coordinate system according to 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; The covariance matrices of the distance measurement error and the velocity error in the Euclidean coordinate system are added together to obtain the comprehensive error covariance matrix in the Euclidean coordinate system.

3. The method according to claim 2, 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 distance measurement error in the Euclidean coordinate system; k represents the proportionality coefficient; 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.

4. The method according to claim 2, 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 unit vector of the velocity direction at the position of the high-speed aircraft anchor node n.

5. The method according to claim 1, characterized in that The method of constructing a high-speed aircraft anchor node clustering model based on a comprehensive error covariance matrix includes: 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 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.

6. The method according to claim 1, characterized in that 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 transformed 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.

7. The method according to any one of claims 1 to 6, characterized in that After optimizing and solving the high-speed aircraft anchor node clustering model to obtain the 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.

8. A high-speed aircraft clustering device for collaborative sensing and positioning, characterized in that: The device comprises: A 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 a high-speed aircraft cluster as anchor nodes; A construction module is used to introduce two positioning error terms during collaborative sensing positioning, namely, 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. 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.

9. 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 7.

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

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