Multi-radar collaborative optimization station distribution method based on improved particle swarm optimization

Abstract

The invention discloses a multi-radar collaborative optimization station distribution method based on an improved particle swarm algorithm, and belongs to the technical field of radars. The method comprises the following steps: constructing a passive time difference positioning model comprising an active radar station and a plurality of passive radar stations; the active radar station is a radar station capable of emitting electromagnetic waves and is located at the origin of coordinates, the passive radar station does not emit the electromagnetic waves, and the station distribution position of the passive radar station is a position to be solved; constructing a target function by taking the minimum sum of the positioning errors of the passive radar stations in the flight airspace of the to-be-tracked target as a target; and solving the station distribution position of each passive radar station based on the objective function and the improved particle swarm optimization. According to the invention, efficient and accurate target positioning and tracking are realized.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, and in particular relates to a multi-radar cooperative optimization deployment method based on an improved particle swarm optimization algorithm. Background Technology

[0002] Multi-radar deployment planning has always been a key issue in radar networking system research. Excellent radar site deployment methods can effectively improve the collaborative performance of multiple radars, achieve effective airspace coverage and stable target tracking, and play an important role in improving mission success rate.

[0003] Multi-radar deployment planning plays a crucial role in achieving effective airspace coverage and stable target tracking. Currently, most technical research on multi-radar deployment planning is rather idealistic, failing to consider the overall tracking and positioning error of multiple radar stations over the target's flight airspace. Furthermore, traditional algorithms are prone to getting stuck in local optima during operation and tend to converge slowly in the later stages of the search process.

[0004] Currently, most technical research on multi-radar deployment planning is rather idealistic, failing to consider the overall tracking and positioning error of multiple radar stations over the target's flight airspace. Furthermore, traditional algorithms are prone to getting trapped in local optima and exhibit slow convergence in the later stages of the search process. Therefore, several problems remain to be addressed regarding multi-radar cooperative optimization deployment methods: (1) How to minimize the overall tracking and positioning error of the target's flight airspace by deploying multiple radar stations; (2) How to achieve global, fast convergence speed multi-radar collaborative optimization deployment. Summary of the Invention

[0005] This invention proposes a multi-radar cooperative optimization deployment method based on an improved particle swarm optimization algorithm to solve the technical problems mentioned above.

[0006] The first aspect of this invention proposes a multi-radar cooperative optimization deployment method based on an improved particle swarm optimization algorithm, the method comprising: Step S1: Construct a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Step S2: Construct an objective function with the goal of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Step S3: Solve the deployment location of each passive radar station based on the objective function and the improved particle swarm algorithm.

[0007] Preferably, the objective function is: in, Let be the objective function. The function is for finding the trace of a matrix. The design matrix for the observation equation, For transpose, ( , , ) represents the location coordinates of the target. , , ( ) represents the position coordinates of the first passive radar station. , , The location coordinates of the second passive radar station, ( , , Let be the position coordinates of the i-th passive radar station. The distance from the target to be tracked to the first passive radar station, The distance from the target to be tracked to the second passive radar station. Let be the distance from the target to be tracked to the i-th passive radar station.

[0008] Preferably, step S3 involves: determining the deployment locations of each passive radar station based on the objective function and an improved particle swarm optimization algorithm, wherein: The improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm, but modifies the weight coefficients to dynamic weight coefficients and incorporates the self-learning coefficients from the particle swarm optimization algorithm. Social learning coefficient Change to dynamic coefficients.

[0009] Preferably, the weighting coefficients are modified to dynamic weighting coefficients, that is: in, w These are the weighting coefficients. As a weighting factor, N For the number of particles, For the number of iterations k Time i1 The local optimal solution for each particle. This is the globally optimal solution.

[0010] Preferably, the self-learning coefficients in the particle swarm optimization algorithm are... Social learning coefficient Modified to dynamic coefficients, including: in, This represents the maximum number of iterations. , These are the first maximum number of learning attempts and the second maximum number of learning attempts, respectively. , These are the first minimum learning factor and the second minimum learning factor, respectively. , The number of iterations is respectively k+1 The values ​​of self-learning coefficient and social learning coefficient.

[0011] A second aspect of this invention proposes a multi-radar cooperative optimization deployment device based on an improved particle swarm optimization algorithm, the device comprising: Initialization module: configured to build a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Objective function construction module: configured to construct an objective function with the objective of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Calculation module: configured to solve for the location of each passive radar station based on the objective function and the improved particle swarm algorithm.

[0012] A third aspect of the present invention provides an electronic device, the electronic device comprising: 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 to enable the at least one processor to perform the method as described above.

[0013] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described above.

[0014] This invention is based on the principle of time-of-flight (TOF) positioning. It establishes a passive TOF positioning model by utilizing the distance difference between the target and each radar station; constructs an objective function to describe and measure the positioning error; and solves the problem using an improved particle swarm optimization algorithm to achieve a multi-radar cooperative optimization deployment method. This invention achieves efficient and accurate target positioning and tracking, possessing significant theoretical and practical value. Attached Figure Description

[0015] Figure 1 A schematic diagram illustrating the steps of the multi-radar cooperative optimization deployment method based on the improved particle swarm optimization algorithm provided by this invention.

[0016] Figure 2 This is a schematic diagram of the geometric positional relationship for time difference measurement and positioning provided by the present invention.

[0017] Figure 3 This is a schematic diagram of the area where the station radar provided by the present invention can be deployed.

[0018] Figure 4 This is a schematic diagram of the radar station deployment range and target flight area provided by the present invention.

[0019] Figure 5 This is a schematic diagram of the radar station deployment range and target flight area provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] like Figure 1 As shown, a multi-radar cooperative optimization deployment method based on an improved particle swarm optimization algorithm is proposed, the method comprising: Step S1: Construct a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Step S2: Construct an objective function with the goal of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Step S3: Solve the deployment location of each passive radar station based on the objective function and the improved particle swarm algorithm.

[0022] Passive radar stations offer good concealment in tracking and locating targets. They do not emit electromagnetic waves themselves, but rather locate and range targets by receiving electromagnetic waves emitted or reflected from the target. Based on the principle of time-difference positioning, when any two radar stations receive electromagnetic signals emitted by a target and measure the time difference, the resulting time difference forms a hyperboloid in three-dimensional space. If there are four stations, three sets of hyperboloids can be formed. Theoretically, these hyperboloids intersect to form two points, one representing the target's true location and the other an ambiguous point. For example... Figure 2 The diagram illustrates the geometric positional relationships of this positioning system. It is assumed that in the radar network, there is one active radar station located at the origin (0,0,0), and the i-th passive radar station is located at... The actual location of the target to be tracked is Then, a passive time difference positioning model can be established for solution.

[0023] Assume that in three-dimensional space, the target location of the target to be tracked is... The location of the passive radar station is Then the Euclidean distance from the target to be tracked to the passive radar station is: (1) set up As the active radar station, other passive radar stations, upon receiving the signal, uniformly transmit the measurement information to the active radar station for data processing. The active radar station performs time difference measurement and positioning calculation. Assuming the propagation speed of the electromagnetic wave signal in free space is c, the following expression can be obtained: (2) in, This represents the actual measured distance difference between the i-th passive radar station and the active radar station to the target being tracked. This represents the actual time difference between the i-th passive radar station and the active radar station to the target being tracked; Let be the distance difference between the i-th passive radar station and the active radar station to the target to be tracked, assuming no measurement error. Let be the actual time difference measurement error between the i-th passive radar station and the active radar station.

[0024] Based on the distance relationships between the stations, the following equation can be established: (3) Passive time-difference positioning (TDD) yields the positioning result by solving for the position of the target to be tracked. This invention's multi-radar cooperative optimization deployment method, based on the principle of minimizing the overall positioning error of passive radar stations over the target's flight airspace, solves for the optimal deployment method of the networked radar stations and calculates the deployment position of each radar station.

[0025] Step S2: The objective function is constructed with the goal of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked. The construction process is as follows: In this invention, the main factors affecting positioning error are time measurement error and station location error. It is assumed that the target to be identified flies at a fixed altitude within an airspace of a defined size and location. It is also assumed that the network of passive radar stations must be deployed within a specified area, and that the terrain data for this area is known. For example, a topographic map of the area where the radar stations can be deployed is shown below. Figure 3 As shown. The antenna installation of a passive radar station is limited by a maximum height; that is, the antenna height can be adjusted within a certain range. Therefore, this must be taken into account when solving for the optimal layout of a passive radar station. An example of the flight area of ​​the target to be identified is shown below. Figure 4 As shown.

[0026] To minimize the overall positioning error of passive radar stations over the target's flight airspace, the flight area is first sampled as discrete points. The average positioning error of all discrete points within the flight area is then used as the objective function. The optimization process minimizes this objective function by using the deployment locations of all passive radar stations and their respective antenna heights as independent variables. Within constraints, an optimization algorithm is used to find the optimal solution. The most commonly used and effective evaluation criterion for describing and measuring positioning error is the Geometrical Dilution of Precision (GDOP). In a three-dimensional Cartesian coordinate system, GDOP is typically defined as: (4) in, , , Let G be the standard deviation of the positioning in the x, y, and z directions, G be the design matrix of the observation scheme, and trace represent finding the trace of the matrix. GDOP reflects the geometric distribution of positioning error in space and can be used as a reference standard for selecting the optimal method. The optimal method selected minimizes the average GDOP value of all discrete points in the target flight area.

[0027] Therefore, the objective function of this invention is: The derivation process is as follows: The distance difference between the i-th passive radar station and the active radar station to the target in flight, assuming no measurement error. Taking the differential, we get the following formula: (5) make: (6) Substituting into equation (5), it can be expressed as: (7) Rewriting equation (7) in matrix form and simplifying the calculation, we get: (8) in: (9) (10) (11) make Because the time errors in measurements between passive and active radar stations are correlated, therefore each There is also a correlation between the observation errors. For station sites, since they are independent of each other, the errors at each station are uncorrelated. Assuming the measurement error has zero mean after system correction, the covariance matrix of the target location estimation error is: (12) In the formula: (13) in, It is the variance of the distance difference measurement error between the i-th passive radar station and the active radar station. for and The correlation coefficient between them, and the error at each site are: All settings can be configured according to the actual situation.

[0028] Therefore, the position error covariance matrix of the radar station is: (14) The position error covariance matrix of the target to be tracked is: (15) According to the definition of GDOP, we can obtain: (16) The objective function has been constructed. The next step is to find the optimal location for radar station deployment to minimize the GDOP value.

[0029] Further, step S3: Solving for the deployment locations of each passive radar station based on the objective function and the improved particle swarm optimization algorithm, wherein: The improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm, but modifies the weight coefficients to dynamic weight coefficients and incorporates the self-learning coefficients from the particle swarm optimization algorithm. Social learning coefficient Change to dynamic coefficients.

[0030] In the particle swarm optimization algorithm, particles update their velocity and position in the following way: (17) (18) in, The self-learning coefficient; The social learning coefficient; , All are random floating-point numbers between [0,1]; The particle position at the k-th iteration. This represents the particle position at the (k+1)th iteration. Let V be the particle velocity at the k-th iteration. The particle velocity at the (k+1)th iteration; This represents the local optimum of an individual particle in the particle swarm. is the global optimal value for the entire particle swarm; w is the weighting coefficient.

[0031] This invention addresses the issue that particle swarm optimization (PSO) algorithms are prone to getting trapped in local optima and experiencing slow convergence in the later stages of the search process. It proposes an improved PSO optimization algorithm, optimizing both the weighting coefficients and the learning factor. Figure 5 As shown.

[0032] Furthermore, the weighting coefficients are modified to dynamic weighting coefficients, that is: (19) in, w These are the weighting coefficients. As a weighting factor, N For the number of particles, For the number of iterations k Time i1 The local optimal solution for each particle. The global optimal solution in, is the weighting factor, and its value is determined based on experience in practical applications; N is the number of particles.

[0033] Furthermore, the self-learning coefficients in the particle swarm optimization algorithm... Social learning coefficient Modified to dynamic coefficients, including: (20) (twenty one) in, This represents the maximum number of iterations. , These are the first maximum number of learning attempts and the second maximum number of learning attempts, respectively. , These are the first minimum learning factor and the second minimum learning factor, respectively.

[0034] The apparatus provided for carrying out the present invention will be described below. The specific implementation process and technical effects are as described above and will not be repeated below.

[0035] Optionally, embodiments of the present invention provide a multi-radar cooperative optimization deployment device based on an improved particle swarm optimization algorithm, the device comprising: Initialization module: configured to build a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Objective function construction module: configured to construct an objective function with the objective of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Calculation module: configured to solve for the location of each passive radar station based on the objective function and the improved particle swarm algorithm.

[0036] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0037] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0038] The modules described above can be connected or communicate with each other via wired or wireless connections. Wired connections may include metal cables, optical fibers, hybrid cables, or any combination thereof. Wireless connections may include connections via LAN, WAN, Bluetooth, ZigBee, or NFC, or any combination thereof. Two or more modules can be combined into a single module, and any module can be divided into two or more units. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here.

[0039] It should be noted that these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Furthermore, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Additionally, these modules can be integrated together to form a System-on-a-Chip (SOC).

[0040] The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0041] The present invention also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0042] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0043] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0044] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0045] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A multi-radar cooperative optimization deployment method based on an improved particle swarm optimization algorithm, characterized in that, The methods include: Step S1: Construct a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Step S2: Construct an objective function with the goal of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Step S3: Solve the deployment location of each passive radar station based on the objective function and the improved particle swarm algorithm.

2. The method as described in claim 1, characterized in that, The objective function is: in, Let be the objective function. The function is for finding the trace of a matrix. The design matrix for the observation equation, For transpose, ( , , ) represents the location coordinates of the target. , , ( ) represents the position coordinates of the first passive radar station. , , The location coordinates of the second passive radar station, ( , , Let be the position coordinates of the i-th passive radar station. The distance from the target to be tracked to the first passive radar station, The distance from the target to be tracked to the second passive radar station. Let be the distance from the target to be tracked to the i-th passive radar station.

3. The method as described in claim 1, characterized in that, Step S3: Based on the objective function and the improved particle swarm optimization algorithm, the deployment locations of each passive radar station are solved, wherein: The improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm, but modifies the weight coefficients to dynamic weight coefficients and incorporates the self-learning coefficients from the particle swarm optimization algorithm. Social learning coefficient Change to dynamic coefficients.

4. The method as described in claim 3, characterized in that, Modify the weighting coefficients to dynamic weighting coefficients, that is: in, w These are the weighting coefficients. As a weighting factor, N For the number of particles, For the number of iterations k Time i1 The local optimal solution for each particle. This is the globally optimal solution.

5. The method as described in claim 4, characterized in that, The self-learning coefficients in the particle swarm optimization algorithm Social learning coefficient Modified to dynamic coefficients, including: in, This represents the maximum number of iterations. , These are the first maximum number of learning attempts and the second maximum number of learning attempts, respectively. , These are the first minimum learning factor and the second minimum learning factor, respectively. , The number of iterations is respectively k+1 The values ​​of self-learning coefficient and social learning coefficient.

6. A multi-radar cooperative optimization deployment device based on an improved particle swarm optimization algorithm, characterized in that, The device includes: Initialization module: configured to build a passive time difference localization model including one active radar station and multiple passive radar stations; the active radar station is a radar station that can emit electromagnetic waves and is located at the origin of the coordinate system, while the passive radar stations do not emit electromagnetic waves and their locations are the locations to be solved. Objective function construction module: configured to construct an objective function with the objective of minimizing the sum of positioning errors of each passive radar station in the flight airspace of the target to be tracked; Calculation module: configured to solve for the location of each passive radar station based on the objective function and the improved particle swarm algorithm.

7. An electronic device, characterized in that, The device includes: 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 to enable the at least one processor to perform the method as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1-5.

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