A radar network target allocation method, apparatus, device, and readable storage medium

Abstract

This invention provides a radar network target allocation method, apparatus, and device, relating to the field of resource scheduling technology for space target surveillance missions. The method includes: acquiring a radar network and multiple targets; randomly generating several allocation schemes, wherein each allocation scheme represents a scheme obtained by assigning the multiple targets to radars in the radar network for detection; and iteratively optimizing the allocation schemes using a simulated annealing hybrid discrete particle swarm optimization algorithm to obtain a final scheme. This invention addresses the technical problem that existing resource scheduling methods based on empirical or semi-empirical models lack autonomy and are difficult to adapt to increasingly demanding space target surveillance missions.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling technology for space target surveillance missions, and more specifically, to a radar network target allocation method, apparatus, device, and readable storage medium. Background Technology

[0002] Space targets refer to various man-made celestial bodies in outer space above 100 km above the Earth's surface, mainly including satellites, manned spacecraft, space stations, space shuttles, as well as booster rockets, protective shields, and space debris that enter space upon launch. When a space target has a sufficient initial velocity at a certain altitude, it will orbit under the influence of Earth's gravity. Based on their orbital altitudes, space targets can be classified into low Earth orbit (LEO), medium Earth orbit (MEO), and high Earth orbit (HEO) targets. In space target surveillance activities, radar equipment, primarily phased array radars, mainly targets MEO and MEO targets. Faced with the rapid increase in the number of space targets, existing resource scheduling methods based on empirical and semi-empirical models lack autonomy and are ill-suited to the increasingly demanding space target surveillance tasks. Summary of the Invention

[0003] The purpose of this invention is to provide a radar network target allocation method, apparatus, device, and readable storage medium to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a radar network target allocation method, including:

[0005] Acquire radar network and multiple targets;

[0006] Several allocation schemes are randomly generated, wherein an allocation scheme represents a scheme that assigns the multiple targets to radars in a radar network for detection;

[0007] The allocation scheme is iteratively optimized using a simulated annealing hybrid discrete particle swarm optimization algorithm to obtain the final scheme.

[0008] Furthermore, the random generation of several allocation schemes specifically includes:

[0009] The constraints for radar target detection are set, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints.

[0010] The radars and targets that meet the constraints are paired up, and an allocation scheme is obtained after all targets and radars are assigned.

[0011] Furthermore, the step of using simulated annealing hybrid discrete particle swarm optimization algorithm to iteratively optimize the allocation scheme specifically includes:

[0012] Construct a target allocation ranking model;

[0013] Each allocation scheme is iteratively updated based on the simulated annealing hybrid discrete particle swarm algorithm, and the allocation scheme with higher fitness after the update is stored as an individual candidate scheme.

[0014] The allocation scheme with higher fitness among several individual candidate schemes is stored as a global candidate scheme.

[0015] Furthermore, the construction of the target allocation ranking model specifically includes:

[0016] Acquire beam pointing energy, target value level, and radar measurement accuracy;

[0017] A target allocation ranking model is constructed, which is used to calculate the fitness of the allocation scheme to evaluate the merits of the allocation scheme.

[0018] Furthermore, it also includes:

[0019] Repeatedly update and iterate the allocation scheme to obtain the global candidate scheme with the highest fitness, until the fitness of the global candidate scheme does not change within the first preset number of iterations or the number of iterations of the allocation scheme reaches the second preset number of iterations;

[0020] The global candidate solution with the highest fitness is selected as the final solution.

[0021] Secondly, this application also provides a radar network target allocation device, comprising:

[0022] Acquisition module: used to acquire radar network data and multiple targets;

[0023] Allocation module: randomly generates several allocation schemes, wherein one allocation scheme represents the scheme of allocating the multiple targets to radars in the radar network for detection;

[0024] Optimization module: Used to iteratively optimize the allocation scheme using simulated annealing and discrete particle swarm optimization algorithms to obtain the final scheme.

[0025] Furthermore, the allocation module includes:

[0026] Setting unit: used to set the constraints for radar target detection, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints;

[0027] Pairing unit: used to pair radars and targets that meet the constraints, and an allocation scheme is obtained after all targets and radars are assigned.

[0028] Furthermore, the optimization module includes:

[0029] Model building unit: Constructing the target allocation and ranking model;

[0030] Model building unit: used to build the target allocation and ranking model;

[0031] The first screening unit is used to iteratively update each allocation scheme based on the simulated annealing hybrid discrete particle swarm algorithm, and to store the allocation scheme with higher fitness after the update as an individual candidate scheme.

[0032] The second filtering unit is used to store the allocation scheme with higher fitness among several individual candidate schemes as global candidate schemes.

[0033] Thirdly, this application also provides a radar networking target allocation device, comprising:

[0034] Memory, used to store computer programs;

[0035] A processor is used to implement the steps of the radar networking target allocation method when executing the computer program.

[0036] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the radar-based target allocation method described above.

[0037] The beneficial effects of this invention are as follows:

[0038] This invention analyzes the resource scheduling principles in space target surveillance missions and constructs a target allocation and ranking model based on these missions. Driven by specific mission requirements, and through research on the characteristics of space targets and phased array radars, it designs constraints for resource scheduling scenarios to address the lack of specificity in existing technologies for space target surveillance missions. Furthermore, this invention introduces particle swarm optimization, simulated annealing, and artificial intelligence algorithms to solve the problem of low autonomy in empirical and semi-empirical model-based resource scheduling methods in practical space target surveillance missions. This enables intelligent resource scheduling for radar networks, promoting the system's development towards autonomous task processing, automatic target allocation, and adaptive command and control.

[0039] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a diagram of the hierarchical structure model described in the embodiments of the present invention;

[0042] Figure 2 This is a schematic diagram of the radar networking target allocation method described in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the three-dimensional allocation matrix described in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the particle coding structure described in an embodiment of the present invention;

[0045] Figure 5 This is a diagram illustrating the final solution described in an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the radar network target allocation device described in an embodiment of the present invention;

[0047] Figure 7 This is a schematic diagram of the radar network target allocation device structure described in an embodiment of the present invention.

[0048] The markings in the diagram are: 800, radar network target allocation equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0050] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] Space target surveillance missions refer to a type of combat mission that uses various detection equipment (including satellites, optoelectronic devices, radar equipment, etc.) to detect the entry, exit, and operation of all man-made celestial bodies in space, and combines this with space target intelligence data to comprehensively process and analyze information such as target orbits, functions, and threats, and generate a full-element space situation to provide information support for space activities.

[0052] Radar networks are used to track and detect targets in space target surveillance missions, enabling information sharing among sensor nodes. Before executing a space target surveillance mission, task planning for the radar network is necessary. The principle of task planning is to optimize the target allocation and sequencing scheme by rationally scheduling the detection resources it contains over time, based on meeting mission requirements and constraints, in order to achieve more accurate and efficient detection of space targets.

[0053] Space target surveillance missions aim to monitor the on-orbit status of space targets and generate a comprehensive space situational awareness based on the acquired orbital information. Therefore, surveillance forces need to accurately determine the orbits of targets with high measurement precision, using this information as the basis for identification and cataloging. In space target surveillance missions, radar networking will prioritize ensuring the accuracy of radar target measurements as a principle of resource allocation.

[0054] Example 1:

[0055] This embodiment provides a radar network target allocation method, which includes the following steps before step S1:

[0056] S0. Prioritize tasks;

[0057] Specifically, S0 includes:

[0058] S01. Using the importance and urgency of the mission, the completeness of prior intelligence, and operational resolve as the criteria layer; and the various operational tasks to be divided as the plan layer (taking three operational tasks as an example), a hierarchical structure model is constructed, such as... Figure 1 As shown;

[0059] S02. Using the Analytic Hierarchy Process (AHP), a set of weight vectors of the criterion layer with respect to the target layer is calculated: w C1 w C2 w C3 w C4; and the four sets of weight vectors from the scheme layer regarding the criterion layer, the first set: w C11 w C12 w C13 Group 2: w C21 w C22 w C23 Group 3: w C31 w C32 w C33 Group 4: w C41 w C42 w C43 As shown in Table 1:

[0060] Table 1

[0061]

[0062] S03. Calculate the priorities of the three combat missions based on the five sets of weight vectors. The calculation method is as follows:

[0063] O Pa =w C1 w C1a +w C2 w C2a +w C3 w C3a +w C4 w C4a (1)

[0064] In the formula, 'a' represents the mission number;

[0065] S04. Prioritize the three combat missions, and execute the plan for the mission with higher priority first. Before executing the plan, target allocation needs to be performed on the radar network in the combat mission.

[0066] See Figure 2 The figure shows that this method includes:

[0067] S1. Acquire radar network and multiple targets in a combat mission;

[0068] The radar network consists of n radars, which monitor m targets within k units of operational time.

[0069] Define a three-dimensional allocation matrix X m*n*k ,like Figure 3 As shown, the three-dimensional allocation matrix represents the tracking status of m targets detected by n radars during combat. In the figure, the tracking status of the same target is represented by elements in the same plane perpendicular to the "target number" coordinate axis. The element x in the matrix... ijt Let x be any value in {0,1}, where x ijt=1 indicates that at combat time t, the j-th radar is assigned to track and detect the i-th target, x ijt =0 indicates that at combat time t, the j-th radar does not track or detect the i-th target.

[0070] S2. Randomly generate several allocation schemes, wherein one allocation scheme represents the scheme of assigning the multiple targets to radars in the radar network for detection;

[0071] Specifically, step S2 includes:

[0072] S21. Set constraints for radar target detection, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints;

[0073] 1) The effective detection range of the radar is the moment when the target enters the radar detection range and the moment when the target leaves the radar detection range. From this, a time constraint condition can be constructed:

[0074]

[0075] In the formula, t represents the time for radar to detect the target. ij0 Let t represent the time when the i-th target enters the radar detection range. ijs This indicates the moment when the i-th target flies out of the radar detection range.

[0076] 2) Construct directional constraints:

[0077]

[0078] In the formula, e ij θ represents the beam pointing angle. j Indicates the azimuth constraint angle.

[0079] 3) Construct capacity constraints:

[0080]

[0081] In the formula, c j Indicates detection capacity;

[0082] 4) During radar network detection, multiple radars jointly detecting the same target can effectively improve the overall collaborative detection effect and enhance the system's robustness through fusion techniques. However, excessive duplicate detection will waste detection resources and create target redundancy. It is generally considered that joint detection by 2-3 radars is reasonable; duplicate detection by more than 3 radars is a waste of resources and violates the principle of fully utilizing detection resources. Therefore, a redundancy constraint is established:

[0083]

[0084] 5) To meet the accuracy requirements for space target monitoring and orbit determination, the maximum continuous detection time of each radar needs to be limited to generate an effective monitoring arc. Therefore, the continuous detection time of the radar on the target must not be less than the effective monitoring arc time. That is, from the start of radar detection of the target, the radar needs to continuously track and detect within the effective monitoring arc time, thus constructing a continuity constraint condition:

[0085]

[0086] In the formula, t min t′ represents the effective monitoring arc duration, and t′ represents the time variable.

[0087] S22. Pair up radars and targets that meet the constraints. After all targets and radars are assigned, an allocation scheme is obtained.

[0088] S23. Repeat the above steps to generate several allocation schemes. Optimizing multiple allocation schemes simultaneously can speed up the process of finding the optimal allocation scheme (final scheme).

[0089] S3. The allocation scheme is iteratively optimized using the simulated annealing hybrid discrete particle swarm optimization algorithm to obtain the final scheme.

[0090] Specifically, step S3 includes:

[0091] S31. Obtain beam pointing energy, target value level, and radar measurement accuracy, and construct a target allocation ranking model. The target allocation ranking model is used to calculate the fitness of the allocation scheme to evaluate its merits.

[0092]

[0093] In the formula, F represents fitness, and w i K1 represents the target value level, indicating the level of value for w. i The weighted value, p j K2 represents the radar measurement accuracy, where K2 represents the accuracy of measurement for p. j The weighted value, e ij K3 indicates that the beam is pointing towards energy, and K3 indicates that the beam is directed towards e. ij The weighted value, w i p j e ij The relative values ​​are obtained after normalization of the actual radar parameters and are determined by the attributes of the radar group. K1, K2, and K3 are flexibly selected according to the specific mission scenario and user needs, representing the different degrees of importance attached to the three factors affecting the target tracking and detection performance.

[0094] It should be noted that the radar measurement accuracy has a greater impact on the model. Therefore, the weight of radar measurement accuracy in the objective function should be increased so that K2 is higher than K1 and K3. The specific value should be set according to the actual situation and scenario.

[0095] S32. Based on the simulated annealing hybrid discrete particle swarm algorithm, each allocation scheme is iteratively updated, and the allocation scheme with higher fitness after the update is stored as an individual candidate scheme;

[0096] In this embodiment, based on the established target allocation and ranking model, the particle swarm algorithm is introduced into the solution of the radar network target allocation problem. Through its organic combination with the model and constraints, and by integrating the optimization idea of ​​the simulated annealing algorithm, a simulated annealing hybrid discrete particle swarm algorithm for target allocation and ranking is constructed.

[0097] A simulated annealing hybrid discrete particle swarm optimization algorithm for target allocation and sorting is constructed, wherein the particles in the simulated annealing hybrid discrete particle swarm optimization algorithm represent an allocation scheme, the individual extreme value represents an individual candidate scheme, and the global extreme value represents a global candidate scheme;

[0098] Since one allocation scheme corresponds to one particle, a particle can be represented as a three-dimensional allocation matrix X. m*n*k 3D allocation matrix X m*n*k It consists of m×n×k elements x ijt The structure consists of a three-dimensional matrix X, where each element represents a dimension and takes values ​​from {0, 1}. m*n*k Transforming the vector particles into a single row of m×n×k columns, we can obtain the particle encoding structure, such as... Figure 4 As shown, suppose there are D elements in the graph, which means there are D dimensions.

[0099] Let X c Let X represent the position vector of any particle c in D-dimensional space, where the elements of the position vector in the D dimensions are represented as X. c =(x c1 ,x c2 ,…,x cd ); the elements x of the position vector of any particle c in D dimensions cd The value can be {0, 1};

[0100] Let V c The velocity of a particle is represented by V, where the velocity is expressed as an element in D dimensions. c =(v c1 ,v c2 ,…,v cd );

[0101] V is converted using the Sigmoid function cdConvert to probability selection parameter s, the range of which is [0,1];

[0102]

[0103] In the formula, k represents the value of the kth iteration, and e represents the beam pointing energy.

[0104] Generate a random parameter ρ∈(0,1);

[0105] x is calculated using the probability selection parameter s and the random parameter ρ. cd :

[0106]

[0107] Let F(X) c () represents the fitness of a particle, that is, the degree of superiority or inferiority of a particle.

[0108] Let P represent best,c Individual extreme value, the element of the individual extreme value in D dimensions is represented by P. best,c =(p c1 ,p c2 ,…,p cd );

[0109] G best Let G represent the global extremum, where the elements of the global extremum in D dimensions are represented by G. best =(g1,g2,…,g d ).

[0110] Step S2 generates M allocation schemes, resulting in M ​​particles, which together form the initial particle swarm. Since each target will be detected by 3 radars throughout the entire combat time, 3×m×k 1 elements are generated in the allocation scheme generation. This ensures that in the m×n×k column, among the 3×m×k 1 elements, the rest are 0 elements, thereby accelerating the optimization process.

[0111] Using the aforementioned allocation scheme as the initial individual extreme values ​​of the particles, the initial fitness of all particles is calculated sequentially using the target allocation ranking model:

[0112] The initial fitness F(X) of each particle is calculated by substituting it into the target assignment ranking model. c1 ), F(X) c2 ...Let the particle with the smallest initial fitness be the global extremum G. best .

[0113] Specifically, the method for iteratively updating particles (schemes) using the simulated annealing hybrid discrete particle swarm optimization algorithm is as follows:

[0114] The initial control temperature of the simulated annealing mechanism is calculated based on the fitness of the global extremum in the initial particle swarm:

[0115]

[0116] In the formula, T0 represents the initial control temperature.

[0117] Define a cooling function to calculate the annealing fitness of all individual extremes:

[0118] T k+1 =λT k (11)

[0119] In the formula, T represents the control temperature;

[0120] Based on the Metropolis criterion, calculate the annealing fitness of all individual extremes:

[0121]

[0122] The cumulative probability function is obtained by summing the annealing fitness of all individual extreme values ​​in turn:

[0123]

[0124] In the formula, n represents the nth particle.

[0125] The current particle is obtained by selecting particles that meet the preset screening conditions using the cumulative probability function, specifically including:

[0126] A parameter pBet∈(0,1) is randomly generated;

[0127] The individual extreme values ​​of the particles are successively substituted into the probability function until the individual extreme value of the r-th particle satisfies fit(r-1). <pBet≤comfit(r);

[0128] The elements p of the individual extreme values ​​of the r-th particle in D dimensions are obtained sequentially. rd ;

[0129] Using element p rd Update the particle's flight speed:

[0130]

[0131] In the formula, w represents the inertia factor, c1 represents the self-learning factor, c2 represents the group learning factor, r1 and r2 both represent random numbers within the range of values, p represents the individual extreme value, and x represents the position vector.

[0132] Substituting the updated flight speed into formulas (8) and (9) yields the particle's position vector;

[0133] It should be noted that a particle completes one update after its position vector and flight velocity in D dimensions have been updated.

[0134] The updated allocation scheme with higher fitness is stored as an individual candidate scheme, specifically:

[0135] The initial fitness values ​​are obtained from the previous allocation scheme;

[0136] Under the constraint condition that the updated particles satisfy the above, the updated particles are substituted back into the target allocation and ranking model to calculate the fitness. The fitness is then compared with the initial fitness value, and the allocation scheme with higher fitness is stored as an individual candidate scheme. The specific calculation formula is as follows:

[0137]

[0138] In the formula, k represents the k-th iteration.

[0139] It should be noted that even if the particles are updated with the goal of achieving both individual and global extrema, there may still be particles that are worse than those that were not updated. In other words, the particles that were not updated may have a higher fitness. In this case, the updated particles should be discarded and the particles that were not updated should be stored as individual candidate solutions.

[0140] If the current particle does not meet the constraints, randomly swap the positions of the elements in the particle to obtain a corrected particle, and then repeat the judgment to see if the constraints are met.

[0141] S33. Store the allocation scheme with higher fitness among several individual candidate schemes as global candidate schemes;

[0142]

[0143] S34. Repeatedly update and iterate the allocation scheme to obtain the global candidate scheme with the highest fitness, until the fitness of the global candidate scheme does not change within the first preset number of iterations or the number of iterations of the allocation scheme reaches the second preset number of iterations.

[0144] The global candidate solution with the highest fitness is selected as the final solution:

[0145] Specifically, step S34 includes:

[0146] S341: Update the particles again based on the simulated annealing hybrid discrete particle swarm algorithm, that is, repeat steps S32-S33.

[0147] S343. Determine whether the iteration stopping condition is met. The iteration stopping condition is that the fitness of the global candidate solution has not changed within a first preset number of iterations or the number of iterations of the allocation solution has reached a second preset number of iterations.

[0148] If the condition is met, the iteration stops, and the global candidate solution with the highest fitness is taken as the final solution. The final solution can be a global candidate solution whose fitness has not changed within a first preset number of iterations. Preferably, the first preset number of iterations is 20 and the second preset number of iterations is 500.

[0149] If the fitness of the global candidate solution remains unchanged for 20 consecutive times or the particles have been updated 500 times, it means that the fitness of the current global candidate solution is the maximum value, which is the optimal allocation solution.

[0150] Otherwise, proceed to step S341 to continue optimizing the particles.

[0151] In this embodiment, during each optimization process, both individual candidate solutions and global candidate solutions are updated simultaneously. Individual candidate solutions are the historical best solutions found by the particle itself, used to represent the current best position of the individual. Global candidate solutions are the historical best solutions found by the entire particle swarm, used to represent the best position of the swarm after all particles have searched.

[0152] The principle of this method is as follows: When allocating targets to a radar network, M allocation schemes are first randomly generated. For each allocation scheme, a simulated annealing hybrid discrete particle swarm optimization algorithm is used to continuously optimize it, thereby achieving the optimal allocation scheme. Fitness is used to evaluate the quality of an allocation scheme; a higher fitness value indicates a better allocation scheme. The simulated annealing hybrid discrete particle swarm optimization algorithm treats each allocation scheme as a particle, and the process of updating the particles is the process of optimizing the allocation scheme. After multiple optimizations, the allocation scheme with the maximum fitness value is the individual candidate scheme.

[0153] Once all M schemes have yielded individual candidate schemes, they are compared again, and the individual candidate scheme with the highest fitness is taken as the global candidate scheme, thus completing one iteration update.

[0154] The individual and global candidate solutions are iterated again until the termination optimization condition is met. The resulting global candidate solution is the optimal solution among all allocation solutions, which is the final solution selected in this task. Figure 5 The figure shows the optimized result of the final solution.

[0155] Example 2:

[0156] like Figure 6 As shown, this embodiment provides a radar network target allocation device, the device comprising:

[0157] Acquisition module: used to acquire radar network data and multiple targets;

[0158] Allocation module: randomly generates several allocation schemes, wherein one allocation scheme represents the scheme of allocating the multiple targets to radars in the radar network for detection;

[0159] Optimization module: Used to iteratively optimize the allocation scheme using simulated annealing and discrete particle swarm optimization algorithms to obtain the final scheme.

[0160] Furthermore, the allocation module includes:

[0161] Setting unit: used to set the constraints for radar target detection, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints;

[0162] Pairing unit: used to pair radars and targets that meet the constraints, and an allocation scheme is obtained after all targets and radars are assigned.

[0163] Furthermore, the optimization module includes:

[0164] Model building unit: used to build the target allocation and ranking model;

[0165] The first screening unit is used to iteratively update each allocation scheme based on the simulated annealing hybrid discrete particle swarm algorithm, and to store the allocation scheme with higher fitness after the update as an individual candidate scheme.

[0166] The second filtering unit is used to store the allocation scheme with higher fitness among several individual candidate schemes as global candidate schemes.

[0167] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0168] Example 3:

[0169] Corresponding to the above method embodiments, this embodiment also provides a radar network target allocation device. The radar network target allocation device described below and the radar network target allocation method described above can be referred to each other.

[0170] Figure 7 This is a block diagram illustrating a radar network target allocation device 800 according to an exemplary embodiment. Figure 7 As shown, the radar network target allocation device 800 may include: a processor 801 and a memory 802. The radar network target allocation device 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0171] The processor 801 controls the overall operation of the radar network target allocation device 800 to complete all or part of the steps in the radar network target allocation method described above. The memory 802 stores various types of data to support the operation of the radar network target allocation device 800. This data may include, for example, instructions for any application or method operating on the radar network target allocation device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the radar network target allocation device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0172] In an exemplary embodiment, the radar network target allocation device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the radar network target allocation method described above.

[0173] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the radar network target allocation method described above. For example, the computer-readable storage medium may be the memory 802 including program instructions, which may be executed by the processor 801 of the radar network target allocation device 800 to complete the radar network target allocation method described above.

[0174] Example 4:

[0175] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in correspondence with the radar networking target allocation method described above.

[0176] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the radar networking target allocation method described in the above method embodiments.

[0177] Specifically, the readable storage medium can be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0178] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0179] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A radar network target allocation method, characterized in that, include: Acquire radar network and multiple targets; Several allocation schemes are randomly generated, wherein an allocation scheme represents a scheme that assigns the multiple targets to radars in a radar network for detection; The allocation scheme is iteratively optimized using a hybrid simulated annealing and discrete particle swarm optimization algorithm to obtain the final scheme, including: By acquiring beam pointing energy, target value level, and radar measurement accuracy, a target allocation and ranking model is constructed. This model is used to calculate the fitness of allocation schemes to evaluate their merits. ； In the formula, Indicates fitness. Indicates the target value level. Indicates to The weighted value, Indicates radar measurement accuracy. Indicates to The weighted value, Indicates that the beam is pointing towards energy. Indicates to The weighted value, Indicates the quantity of a unit of combat time. Indicates the total number of targets. Indicates the total number of radars. Indicates the first In the first combat moment, is it determined by the first... Radar for the first Detect each target; Each allocation scheme is iteratively updated based on the simulated annealing hybrid discrete particle swarm algorithm, and the allocation scheme with higher fitness after the update is stored as an individual candidate scheme. Store the allocation scheme with higher fitness among several individual candidate schemes as the global candidate scheme; The allocation scheme is repeatedly updated and iterated to obtain the global candidate scheme with the highest fitness, until the fitness of the global candidate scheme does not change within a first preset number of iterations or the number of iterations of the allocation scheme reaches a second preset number of iterations. The global candidate scheme with the highest fitness is then taken as the final scheme.

2. The radar network target allocation method according to claim 1, characterized in that, The random generation of several allocation schemes specifically includes: The constraints for radar target detection are set, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints. The radars and targets that meet the constraints are paired up, and an allocation scheme is obtained after all targets and radars are assigned.

3. A radar network target allocation device, used in the radar network target allocation method according to any one of claims 1 to 2, characterized in that, include: Acquisition module: used to acquire radar network data and multiple targets; Allocation module: randomly generates several allocation schemes, wherein one allocation scheme represents the scheme of allocating the multiple targets to radars in the radar network for detection; Optimization module: Used to iteratively optimize the allocation scheme using simulated annealing and discrete particle swarm optimization algorithms to obtain the final scheme.

4. The radar networking target allocation device according to claim 3, characterized in that, The allocation module includes: Setting unit: used to set the constraints for radar target detection, including time constraints, direction constraints, capacity constraints, redundancy constraints, and continuity constraints; Pairing unit: used to pair radars and targets that meet the constraints, and an allocation scheme is obtained after all targets and radars are assigned.

5. A radar network target allocation device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the radar networking target allocation method as described in any one of claims 1 to 2 when executing the computer program.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the radar networking target allocation method as described in any one of claims 1 to 2.

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