Airborne radar networking multi-task cooperative planning method and device, equipment and medium

Abstract

This application belongs to the field of radar networking technology, specifically disclosing a method, apparatus, equipment, and medium for multi-task collaborative planning of airborne radar networking. The method includes: calling a pre-constructed multi-task collaborative planning model for airborne radar networking; the model is established with the goal of maximizing the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the task echo, and the system detection performance; the joint interception probability of the networked radars is determined based on the interception probability when the window functions of the radiated signals of each radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain; and solving the multi-task collaborative planning model using the detection range parameters of the current airborne radar network to determine the current task allocation results for each radar. This application can effectively improve the execution efficiency of multi-task collaborative planning of radar networking while realizing multi-task collaborative planning for multiple radars.

Description

Technical Field

[0001] This application belongs to the field of radar networking technology, and more specifically, relates to a method, apparatus, equipment and medium for multi-task collaborative planning of airborne radar networking. Background Technology

[0002] Faced with the high complexity, unpredictability, and strong information requirements of the open ocean environment, traditional land-based mono-base radar systems are ill-suited to meet the needs of these scenarios due to limitations such as limited detection range, weak dynamic resource reconfiguration capabilities, and poor anti-interference capabilities. In contrast, distributed, decentralized airborne radar networks can utilize different frequency bands, time, space, and polarization modes to provide comprehensive environmental situational awareness of target information. They can effectively perform multiple tasks in cluttered environments, demonstrating strong resilience and robustness, and offering significant advantages for target search, tracking, confirmation, and guidance applications. Therefore, the multi-task planning problem of radar network systems has gradually become a research hotspot.

[0003] However, most existing research is based on single-radar node mission scenarios, and research on collaborative detection mission planning in generalized multi-radar networks and multi-mission environments is relatively limited. Existing research on multi-aircraft radar multi-mission collaborative detection mission allocation usually considers overly simple and limited performance constraints, ultimately resulting in low execution efficiency of radar network multi-mission collaborative systems.

[0004] Therefore, how to better achieve collaborative planning of multiple tasks in airborne radar networking has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this application is to better realize the collaborative planning of multiple tasks in airborne radar networking, and to solve the problem that the execution efficiency of existing radar networking multi-task collaborative systems is not high.

[0006] To achieve the above objectives, in a first aspect, this application provides a multi-task cooperative planning method for airborne radar networking, comprising: The pre-constructed airborne radar network multi-task collaborative planning model is invoked. This model is established to maximize the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the mission echo, and the system detection performance. The joint interception probability of the networked radars is determined based on the interception probability when the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain window functions. The detection range parameters of the current airborne radar network are used to solve the multi-task collaborative planning model of the airborne radar network to determine the current task allocation results of each network radar.

[0007] Optionally, the joint interception probability of the networked radars is determined through the following steps: Based on the detection probability model of the intercept receiver for radar radiated signals, the first probability of the radiated signals of each of the networked radars being intercepted in the power domain and the corresponding average window width are determined. Based on the radar radiation model, the second probability of the radiation signal of each of the networked radars being intercepted in the time domain and the corresponding average window width are determined. Based on the state of the radar signal pointing to the intercept receiver of each of the networked radars, the third probability of the radiated signal of each of the networked radars being intercepted in the airspace and the corresponding average window width are determined. Based on the detection parameters of the intercept receiver and the radar operating frequency of each of the network radars, the fourth probability of the radiated signal of each of the network radars being intercepted in the frequency domain and the corresponding average window width are determined. Based on each of the first probabilities and their corresponding average window widths, each of the second probabilities and their corresponding average window widths, each of the third probabilities and their corresponding average window widths, and each of the fourth probabilities and their corresponding average window widths, the interception probability of each network radar when the window functions in the power domain, time domain, spatial domain, and frequency domain simultaneously overlap is determined, and the joint interception probability of the network radar is determined according to the interception probability corresponding to each network radar.

[0008] Optionally, determining the first probability of interception of the radiated signal of each of the networked radars in the power domain and the corresponding average window width based on the detection probability model of the intercepting receiver for the radar radiated signal includes: Obtain the signal-to-noise ratio of each of the networked radars detected by the intercept receiver; By inputting the given false alarm probability and each of the networked radars into the detection probability model, a first probability is obtained that the radiated signal of each of the networked radars is intercepted in the power domain. Based on the first probability and the corresponding radiation period of each of the networked radars, the average window width at which the radiated signal of each of the networked radars is intercepted in the power domain is determined.

[0009] Optionally, determining the second probability of the radiated signal of each of the networked radars being intercepted in the time domain and the corresponding average window width based on the radar radiation model includes: Based on the radar radiation model, the radiation time of each network radar to the intercepting receiver, the radiation period, pulse width and pulse repetition period of each network radar are determined. Based on the radiation time of each network radar to the intercepting receiver, the pulse width and pulse repetition period of each network radar, the average window width of the radiation signal of each network radar intercepted in the time domain is determined. By calculating the ratio of the average window width to the corresponding radiation period for each of the networked radars, the second probability of the radiation signal of each of the networked radars being intercepted in the time domain is determined.

[0010] Optionally, the detection parameters include instantaneous bandwidth and detection frequency band; determining the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain and the corresponding average window width based on the detection parameters of the intercept receiver and the radar operating frequency of each network radar includes: Determine the ratio of the instantaneous bandwidth of the intercepting receiver to the detection frequency band; Determine the product of the ratio and the radar operating frequency of each of the networked radars; Based on the product of each of the networked radars, the fourth probability of the radiated signal of each of the networked radars being intercepted in the frequency domain is obtained. Based on the fourth probability and the corresponding radiation period of each of the networked radars, the average window width at which the radiated signal of each of the networked radars is intercepted in the frequency domain is determined.

[0011] Optionally, the step of solving the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network to determine the current task allocation results of each network radar includes: Initialize the particle swarm with radar-mission node scheduling parameters as optimization variables; Based on the aforementioned airborne radar networking multi-task collaborative planning model, the target fitness function of the particle swarm optimization algorithm is determined. With the goal of maximizing the fitness value of the target fitness function, the particle swarm optimization algorithm is iteratively solved using the particle swarm and the detection distance parameter to obtain the globally optimal particle. Based on the globally optimal particle, the optimal solution corresponding to the radar-task node scheduling parameters is obtained; The current task allocation result of each network radar is obtained based on the optimal solution corresponding to the radar-task node scheduling parameters.

[0012] Optionally, the particle swarm optimization algorithm is obtained by improving the velocity update method in the original particle swarm optimization algorithm by introducing a compression factor; the compression factor is determined based on the algorithm learning factor.

[0013] Secondly, this application provides an airborne radar networking multi-mission collaborative planning device, comprising: The model invocation module is used to invoke the constructed airborne radar network multi-task collaborative planning model. This model is established to maximize the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the mission echo, and the system detection performance. The joint interception probability of the networked radars is determined based on the interception probability when the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain window functions. The model solving module is used to solve the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network, and to determine the current task allocation results of each network radar.

[0014] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0016] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0017] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0018] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: This application provides a method, apparatus, equipment, and medium for multi-task collaborative planning of airborne radar networks. Considering the significant impact of radar survivability in distant maritime environments on the overall performance of airborne radar network systems, it introduces the joint intercept probability of multi-task networked radars as an evaluation index for the stealth capability of individual radars from four dimensions: power domain, time domain, spatial domain, and frequency domain. Combining the mission echo signal-to-noise ratio and system detection performance as constraints, a multi-task collaborative planning model for airborne radar networks is established with the goal of maximizing the collaborative execution efficiency of airborne radar missions. This model adaptively optimizes the allocation of multiple tasks for multiple networked radars, effectively improving the execution efficiency of multi-task collaborative planning of radar networks while realizing multi-task collaborative planning for multiple radars, thus achieving efficient resource scheduling for airborne radars. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the multi-task collaborative planning method for airborne radar networking provided in this application embodiment; Figure 2 This is a schematic diagram of the radar radiation model provided in the embodiments of this application; Figure 3 This is a schematic diagram of the distribution of airborne radar and the trajectory of target movement provided in the embodiments of this application; Figure 4 This is a schematic diagram of the distribution of the search spatial domain provided in the embodiments of this application; Figure 5 This is a schematic diagram of the task allocation result of radar 1 provided in an embodiment of this application; Figure 6 This is a schematic diagram of the task allocation result of radar 2 provided in the embodiments of this application; Figure 7 This is a schematic diagram of the task allocation result of radar 3 provided in the embodiments of this application; Figure 8 This is a schematic diagram of the task allocation result of radar 4 provided in the embodiments of this application; Figure 9 This is a schematic diagram of the task allocation result of radar 5 provided in the embodiments of this application; Figure 10 This is a schematic diagram showing the comparison between the airborne radar networking multi-task cooperative planning method provided in this application embodiment and other algorithms; Figure 11 This is a schematic diagram showing the comparison between the discrete compression factor particle swarm algorithm provided in this application embodiment and other traditional particle swarm algorithms; Figure 12 This is a schematic diagram of the structure of the airborne radar networking multi-task collaborative planning device provided in the embodiments of this application; Figure 13 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first probability" and "second probability," etc., are used to distinguish different probabilities, not to describe a specific order of probabilities.

[0022] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0023] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0024] The embodiments of this application are described below with reference to the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating the multi-task cooperative planning method for airborne radar networking provided in this application embodiment, as shown below. Figure 1 As shown, it includes: Step S1: Invoke the constructed airborne radar network multi-task collaborative planning model; wherein, the airborne radar network multi-task collaborative planning model is established with the goal of maximizing the collaborative execution efficiency of airborne radar tasks by using the joint interception probability of networked radars, the signal-to-noise ratio of mission echoes and the system detection performance as constraints; the joint interception probability of networked radars is determined based on the interception probability when the window functions of the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain and frequency domain; Step S2: Solve the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network to determine the current task allocation results of each network radar.

[0026] Specifically, the joint intercept probability of the networked radars described in the embodiments of this application is used to evaluate the stealth capability of the entire airborne radar network system in the context of far-sea and far-field applications. Specifically, it can be determined based on the intercept probability when the window functions of the radiated signals of each networked radar coincide in the power domain, time domain, spatial domain, and frequency domain.

[0027] The signal-to-noise ratio (SNR) described in this application is used to characterize the quality of the echo signal during radar mission detection. It should be noted that, since radar detection efficiency is significantly affected by echo signal quality in practice, the required SNR for the echo signal can be considered to differ under different operating modes.

[0028] The system detection performance described in this application refers to the detection performance of the entire airborne radar network system, which may specifically include the maximum number of detection beams that each network radar can generate. Here, each detection beam is used to perform a different task.

[0029] The airborne radar networking multi-task collaborative planning model described in this application is a model established with the goal of maximizing the collaborative execution efficiency of airborne radar tasks, based on constraints such as the joint interception probability of networked radars, the signal-to-noise ratio of mission echoes, and the system detection performance. It can be used as a measure of the comprehensive execution efficiency of multi-task airborne radar networking systems.

[0030] The detection range parameters described in the embodiments of this application refer to the airspace center distance parameters between each network radar and each detection mission in the airborne radar network system.

[0031] In the embodiments of this application, in step S1, for the airborne radar networking system, a multi-task collaborative planning model for airborne radar networking needs to be pre-constructed. First, the joint intercept probability of the networked radars can be used to evaluate the stealth capability of the entire radar networking system. Considering the requirements of the far-sea and distant-area application environment, a multi-task networked radar joint intercept probability evaluation index is constructed, with radar-task node scheduling parameters as optimization variables. ,in, Indicates in Time Radar For exploration mission Radar-mission node scheduling parameters.

[0032] Since radar detection efficiency is significantly affected by echo signal quality, the required signal-to-noise ratio (SNR) of the echo signal can be considered to differ under different operating modes. Therefore, establishing a reasonable multi-task echo SNR evaluation index is of great significance. This application constructs a multi-task echo SNR measurement index with radar-task node scheduling parameters as optimization variables. Specifically, it can be expressed as: (1); In the formula, Indicates the radar pulse repetition period. Indicates radar transmit power. and These represent the gains of the transmitting and receiving antennas, respectively. Indicates the receiver processing gain. Indicates the wavelength of the transmitted signal. Indicates the radar node relative to the detection mission Radar Cross Section (RCS) of a target. Represents the Boltzmann constant. This indicates the noise temperature of the radar receiver. This indicates the bandwidth of the matched filter for each radar receiver. This represents the noise figure of the receiver. express Constant detection mission airspace center location and radar node The distance between locations.

[0033] Furthermore, considering that most detection task performance indicators, such as the detection probability characterizing search performance in search tasks and the Bayesian Cramer-Rao lower bound characterizing tracking performance in tracking tasks, are positively correlated with the radar receiver signal-to-noise ratio (SNR), and that the echo SNR is mainly affected by range when parameters such as antenna aperture, gain, and receiver sensitivity of network nodes are fixed, this application, in conjunction with the mission quality framework, adopts a global utility function for allocating airborne networked radar cooperative detection tasks as a measure of the cooperative execution effectiveness of airborne radar tasks. Specifically, it can be expressed as: (2); In the formula, , which means Each radar node is constantly performing detection tasks The assignment vector; Indicates the first The weight of each detection mission; express Each radar node at any time To each exploration mission Minimum distance from the center of the mission airspace; Indicate each radar node With exploration mission The vector consisting of the spatial center distances.

[0034] Furthermore, in the embodiments of this application, based on the multi-beam capability of airborne phased array radar, it is possible to simultaneously detect multiple targets. Optimizing radar-task node scheduling parameters is of great significance for detection mission planning. This application takes maximizing the global utility function of airborne networked radar cooperative detection mission allocation as the optimization objective, and uses constraints such as given networked radar joint intercept probability, mission echo signal-to-noise ratio, and system detection performance as constraints to establish an airborne networked radar detection mission planning model based on multi-task cooperation. That is, the airborne radar network multi-task cooperative planning model is obtained, which can be specifically expressed as:

[0035] (3); In the formula, This indicates the maximum number of beams that a single radar node can generate. This indicates the role of each radar node in the detection mission. The echo signal-to-noise ratio, Indicates the current task type The probability requirement for joint interception of networked radars Indicates the current task type The task's echo signal-to-noise ratio requirements This indicates the total number of tasks. This indicates the total number of radars.

[0036] The first constraint in the aforementioned multi-task cooperative planning model for airborne radar networking indicates that each radar can generate at most [number of] [tasks]. The first constraint specifies that each detection task is performed only once; the second constraint specifies the joint interception probability requirement of the networked radars for the current task type; the third constraint specifies the echo signal-to-noise ratio requirement for the airborne radar to perform the current task; and the fifth constraint specifies the allocation result of the radar-task node scheduling parameters. It is a binary variable.

[0037] In this embodiment, addressing the limitations of traditional land-based radar sensor resource management models that focus solely on system detection capabilities for optimization, which are ill-suited to the complex electromagnetic environments of distant seas and regions, the flexible mobility of carriers, and the distributed collaboration of multiple nodes, this application proposes a design philosophy that transforms the system from "single-function optimization" to "offensive and defensive collaborative management." Based on this, a multi-dimensional evaluation index system is constructed, encompassing "joint interception probability of networked radars" (measuring the system's stealth protection capability), "mission echo signal-to-noise ratio" (measuring the system's guaranteed detection accuracy), and "mission collaborative execution efficiency" (measuring the system's evaluation of multi-node cooperation efficiency). By scheduling system resources such as detection nodes, detection time, and sensor beams, a three-in-one evaluation index framework of "survivability, detection performance, and collaborative efficiency" is formed. With maximizing the overall collaborative detection efficiency of airborne networked radars as the core optimization objective, practical application constraints such as the maximum number of radar beams and the minimum joint interception probability are incorporated to establish a nonlinear discrete optimization model. This leads to the construction of the aforementioned airborne radar resource management model adapted to distant sea and region scenarios—namely, the airborne radar network multi-mission collaborative planning model.

[0038] Furthermore, in the embodiments of this application, in step S1, when performing multi-task collaborative planning for airborne radar networking, the constructed multi-task collaborative planning model for airborne radar networking can be invoked.

[0039] Furthermore, in the embodiments of this application, considering that the above-mentioned model solving problem is about integer variables... This is a nonlinear discrete optimization problem, and it is a nondeterministic polynomial (NP-hard) problem that cannot be solved by an algorithm with polynomial time complexity. Based on this, a heuristic swarm intelligence algorithm can be used to solve the above-mentioned airborne radar networking multi-task cooperative planning model proposed in the embodiments of this application.

[0040] More specifically, in the embodiments of this application, in step S2, the relevant parameters in the above-mentioned airborne radar network multi-task collaborative planning model are assigned values ​​using the detection range parameters of the current airborne radar network. Then, optimization algorithms such as particle swarm optimization can be used to iteratively solve the airborne radar network multi-task collaborative planning model, and finally the optimal solution of the algorithm is determined to obtain the current task allocation results of each network radar.

[0041] The airborne radar networking multi-task collaborative planning method of this application embodiment considers the significant impact of radar survivability in far-sea and far-field environments on the overall execution efficiency of the airborne radar networking system. It introduces the joint interception probability of multi-task networking radars as an evaluation index to measure the stealth capability of a single radar from four dimensions: power domain, time domain, spatial domain, and frequency domain. Combined with the mission echo signal-to-noise ratio and system detection performance as constraints, an airborne radar networking multi-task collaborative planning model is established with the goal of maximizing the collaborative execution efficiency of airborne radar missions. It adaptively optimizes the allocation of multiple tasks for multiple networking radars, which can effectively realize multi-task collaborative planning of multiple radars, and at the same time effectively improve the execution efficiency of radar networking multi-task collaboration, and realize efficient resource scheduling of airborne radars.

[0042] Based on the above embodiments, as an optional embodiment, the network radar joint interception probability mentioned in step S1 is specifically determined through the following steps: Based on the detection probability model of the intercept receiver for radar radiated signals, the first probability of the radiated signals of each network radar being intercepted in the power domain and the corresponding average window width are determined. Based on the radar radiation model, the second probability of the radiation signal of each network radar being intercepted in the time domain and the corresponding average window width are determined. Based on the state of the radar signal pointing to the intercept receiver of each network radar, the third probability of the radiated signal of each network radar being intercepted in the airspace and the corresponding average window width are determined. Based on the detection parameters of the intercept receiver and the radar operating frequency of each network radar, the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain and the corresponding average window width are determined. Based on each first probability and its corresponding average window width, each second probability and its corresponding average window width, each third probability and its corresponding average window width, and each fourth probability and its corresponding average window width, the interception probability of each network radar when the window functions of the radiated signals in the power domain, time domain, spatial domain, and frequency domain all coincide is determined, and the joint interception probability of the network radar is determined according to the interception probability corresponding to each network radar.

[0043] It should be noted that in the context of applications in the far seas and distant areas, environmental resources are at a disadvantage relative to mission requirements. Therefore, the survivability of radar is of extremely important practical significance for releasing the overall application effectiveness.

[0044] Specifically, in the embodiments of this application, the process of intercepting the receiver to capture airborne radar signals can be abstracted as a geometric probability problem in a multidimensional probability space. A window function model can be introduced to describe this process, and a joint interception probability function of the networked radar can be constructed with four types of window functions: power, frequency, time, and space, as the core, and used as an evaluation index of the joint interception probability of the networked radar.

[0045] According to radar radiation models, such as Figure 2 As shown, Indicates the radar radiation period. The pulse width. The pulse repetition period, This indicates the time it takes for the radar transmitter to irradiate the interceptor, also known as the target irradiation time.

[0046] Considering the dynamic changes in power, time, space, and frequency domains of both networked radar and enemy reconnaissance equipment (interception receivers) during the confrontation, an analysis of the interception window functions in these four dimensions reveals that they are not physically existing time windows, but rather conceptual window functions. Since the four dimensions mentioned (power, time, space, and frequency) have different dimensions, they need to be normalized, and the normalization benchmark can be set as the radar radiation period. .

[0047] Specifically, the window function can be defined as follows: , =1,2,3,4; where, This represents the interception window function in the power domain. This represents the interception window function in the time domain. This represents the corresponding interception window function in the spatial domain. This represents the interception window function in the frequency domain. Indicates different domain interception conditions The average window function period. For normalization processing, , , and All can be set as normalization benchmarks. .

[0048] in addition, Indicates different domain interception conditions The average window width within each radar radiation cycle. Assuming that the interception conditions are relatively independent, the interception receiver can only be considered to have successfully intercepted the radar signal when the non-zero parts of all window functions coincide. Therefore, the power, frequency, time, and space window functions involved in this application satisfy the condition of mutual independence.

[0049] Furthermore, in the embodiments of this application, the corresponding interception window function in the power domain is determined. Specifically, power interception probability analysis can be performed based on the detection probability model of the interception receiver for radar radiated signals to determine the first probability of interception of the radiated signals of each network radar in the power domain and the corresponding average window width.

[0050] Based on the above embodiments, as an optional embodiment, based on the detection probability model of the intercept receiver for radar radiated signals, the first probability of interception of the radiated signals of each network radar in the power domain and the corresponding average window width are determined, including: Obtain the signal-to-noise ratio of each network radar detected by the intercept receiver; By inputting the given false alarm probability and each network radar into the detection probability model, the first probability of the radiated signal of each network radar being intercepted in the power domain is obtained. Based on the first probability and corresponding radiation period of each network radar, the average window width of the radiated signal intercepted by each network radar in the power domain is determined.

[0051] Specifically, in the embodiments of this application, when the radar main lobe is aligned with the target for tracking, the power interception probability is... This can be viewed as a given false alarm probability The probability of intercepting the radar emitted signal by the receiver. Detection probability It is computable, and based on the detection probability model of the interceptor for radar radiated signals, it can be specifically expressed as: (4); Next, the interception equation at the receiver input can be expressed as: (5) In the formula, To intercept the radar nodes detected by the receiver radar radiated power, For radar nodes The peak power of the radar radiated pulse, Indicates the radar node in the direction of the intercepted receiver. Antenna gain of radar radiation To intercept the receiver at the radar node The gain of the receiving antenna in the direction of the radar. In order to intercept the operating wavelength of the receiver, This indicates the net gain of the intercepted receiver processor. To determine the distance between the receiver and the radar.

[0052] Next, the noise power intercepted at the receiver input can be expressed as: (6); In the formula, Boltzmann's constant, For noise temperature, To intercept the receiver bandwidth.

[0053] The noise figure can be expressed as: (7); Therefore, the signal-to-noise ratio at the output of the intercept receiver can be expressed as: (8); Furthermore, given a false alarm probability The detection probability is obtained from equation (4). That is, to obtain the first probability of the radiated signal of each network radar being intercepted in the power domain. It can be represented as: (9); After normalization, take Then you can get the corresponding average window width. ,Right now: (10); The method in this application introduces a theoretical model of the detection probability of radar radiated signals by the intercept receiver, and analyzes the probability of interception of the radiated signals of each network radar in depth from the power domain. This ensures the accuracy and reliability of the interception probability and the corresponding average window width data, which is beneficial to improving the accuracy of subsequent joint interception probability estimation of network radars.

[0054] Furthermore, in the embodiments of this application, the corresponding interception window function in the time domain is determined. Specifically, time-domain interception probability analysis can be performed based on the radar radiation model to determine the second probability of the radiated signal of each network radar being intercepted in the time domain and the corresponding average window width.

[0055] Based on the above embodiments, as an optional embodiment, based on the radar radiation model, the second probability of the radiated signal of each network radar being intercepted in the time domain and the corresponding average window width are determined, including: Based on the radar radiation model, the radiation time, radiation period, pulse width, and pulse repetition period of each network radar to the intercept receiver are determined. Based on the radiation time of each network radar to the intercept receiver, the pulse width and pulse repetition period of each network radar, the average window width of the radiated signal of each network radar in the time domain is determined. By calculating the ratio of the average window width to the corresponding radiation period for each network radar, the second probability of the radiated signal of each network radar being intercepted in the time domain is determined.

[0056] Specifically, in the embodiments of this application, based on the aforementioned radar radiation model, the radiation time of each network radar to the intercepting receiver is calculated. Pulse width of each network radar and pulse repetition period This allows us to determine the width of the interception window function in the time domain, which is the average window width at which the radiated signals of each network radar are intercepted in the time domain. It can be represented as: (11); Furthermore, by calculating the average window width and corresponding radiation period for each network radar, The ratio is used to determine the second probability of the radiated signal of each network radar being intercepted in the time domain. This process can be represented as: (12); The method in this application, by introducing a radar radiation model, deeply analyzes the propagation characteristics of radar radiation signals in the time domain, determines the probability of interception of each network radar radiation signal in the time domain and the average window width, which can ensure the accuracy and reliability of the interception probability and the corresponding average window width data, and is conducive to further improving the accuracy of subsequent joint interception probability estimation of network radars.

[0057] Furthermore, in the embodiments of this application, the corresponding interception window function in the spatial domain is determined. Here, in the "counterattack" between the networked radar and the electronic warfare equipment (interception receiver), if the radar beam can illuminate the receiver, it can be determined that the airspace interception conditions are met. Since most reconnaissance interception receivers adopt a wide azimuth design, it is only necessary to determine whether the radar's transmitted beam is pointing towards the reconnaissance equipment.

[0058] When the networked radar is operational, it prioritizes tracking targets with the highest threat level and the greatest global detection benefit. Its transmitted beam continuously illuminates the target, and the target remains within the half-power beamwidth of the antenna's main lobe. In this state, it can be approximated that the radar beam is aligned with the reconnaissance equipment. The corresponding airspace intercept probability, i.e., the third probability that the networked radar's radiated signal is intercepted in the airspace, is... If other targets exist in space, these targets can only acquire radar signals through radar sidelobes. Typically, the power of the first sidelobe of a tracking radar is 13 dB lower than the main lobe (i.e., a power difference of more than 20 times), and the target is further away from the carrier aircraft. Under the premise of controllable power, this can be considered the third probability in this scenario. .

[0059] In the embodiments of this application, normalization processing is performed to obtain... Then the average window width at which the radiated signals of each network radar are intercepted in the airspace can be obtained. It can be represented as: (13); Furthermore, in the embodiments of this application, the corresponding interception window function in the frequency domain is determined. Specifically, frequency domain interception probability analysis can be performed based on the detection parameters of the interception receiver and the radar operating frequencies of each network radar to determine the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain and the corresponding average window width.

[0060] Based on the above embodiments, as an optional embodiment, the detection parameters include instantaneous bandwidth and detection frequency band; based on the detection parameters of the intercept receiver and the radar operating frequency of each network radar, the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain and the corresponding average window width are determined, including: Determine the ratio of the instantaneous bandwidth of the intercepting receiver to the detection frequency band; Determine the product of the ratio and the radar operating frequency of each network radar; Based on the product of each network radar, the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain is obtained. Based on the fourth probability and the corresponding radiation period of each network radar, the average window width of the radiated signal intercepted in the frequency domain of each network radar is determined.

[0061] Specifically, in the embodiments of this application, if the radar operating frequency is within the frequency reconnaissance band of the reconnaissance equipment, then the radiated signal is considered to be interceptable at the frequency domain level, i.e. Otherwise, it cannot be intercepted, that is... .

[0062] More specifically, in embodiments of this application, the instantaneous bandwidth of the intercepting receiver is determined. The ratio to the detection frequency band, which can be obtained by subtracting the highest and lowest frequencies of the radar's operating frequency band detected by the intercepting receiver. Further, this ratio is then compared with the radar operating frequency of each network radar. The product of these factors, and based on the product corresponding to each network radar, yields the fourth probability of the radiated signal of each network radar being intercepted in the frequency domain. This process can be represented as: (14); in, This indicates the instantaneous frequency measurement bandwidth of the intercepted receiver. , These are the lowest and highest frequencies of the radar operating frequency band detected by the interceptor receiver, respectively.

[0063] Furthermore, it is normalized and taken... The fourth probability and corresponding radiation period for each network radar By performing a product operation, the average window width at which the radiated signals of each network radar are intercepted in the frequency domain can be obtained. ,Right now: (15); The method in this application embodiment calculates the degree of matching between the instantaneous bandwidth of the intercept receiver and the operating frequency of the radar signal, and analyzes in depth in the frequency domain the probability of interception of the radiated signals of each network radar, ensuring the accuracy and reliability of the interception probability and the corresponding average window width data, which is conducive to further improving the accuracy of subsequent joint interception probability estimation of network radars.

[0064] Furthermore, according to window function theory, when there are four independent window functions, their average window width coincides. It can be represented as: (16); Therefore, the average period of the four window functions coinciding simultaneously can be expressed as: (17); Thus in The probability that the radar radiation signal is intercepted at least once within a given time period is: (18); In summary, the probability of interception It is an evaluation index for measuring the stealth capability of a single radar within a specific time period. In airborne radar network systems, the joint intercept probability of the networked radars needs to be used to evaluate the stealth capability of the entire radar network. Considering the requirements of the far-sea and far-field application environment, this application constructs a multi-mission networked radar joint intercept probability evaluation index with radar-mission node scheduling parameters as optimization variables, which can be specifically expressed as: (19); In the formula, Indicates in Real-time network radar For exploration mission Radar-mission node scheduling parameters, Indicates deadline Within a certain time period The probability of interception by a network of radars.

[0065] Here, it is understandable that... .

[0066] The method in this application, considering the significant impact of radar survivability in distant sea and far-field environments on the overall performance of airborne radar networking systems, introduces a window function model. This model abstracts the process of the interceptor capturing airborne radar signals as a geometric probability problem in a multi-dimensional probability space. Using four types of window functions—power, frequency, time, and space—as the core, a joint intercept probability function for the networked radars is constructed. This function serves as an evaluation index for the intercept probability of airborne networked radars and is used to constrain the maximization of the global utility of airborne radar resource scheduling. This approach can accurately and efficiently improve the performance of multi-task collaborative radar networking.

[0067] Based on the above embodiments, as an optional embodiment, step S2 involves solving the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network to determine the current task allocation results for each network radar, including: Initialize the particle swarm with radar-mission node scheduling parameters as optimization variables; Based on the multi-task collaborative planning model of airborne radar networking, the target fitness function of the particle swarm optimization algorithm is determined. With the goal of maximizing the fitness value of the target fitness function, the particle swarm optimization algorithm is iteratively solved using particle swarm optimization and probe distance parameters to obtain the globally optimal particle. Based on the globally optimal particle, the optimal solution corresponding to the radar-task node scheduling parameters is obtained; The current task allocation results for each network radar are obtained based on the optimal solution corresponding to the radar-task node scheduling parameters.

[0068] Specifically, in the embodiments of this application, traditional swarm intelligence optimization algorithms, such as particle swarm optimization or genetic algorithms, can be used to solve the aforementioned airborne radar networking multi-task collaborative planning model and determine the current task allocation results of each network radar.

[0069] Alternatively, by further considering traditional swarm intelligence optimization algorithms, such as the traditional particle swarm optimization algorithm, when directly applied to discrete problems, continuous positions need to be forcibly mapped to discrete solutions (such as rounding), which leads to problems of low search efficiency and low solution accuracy.

[0070] Based on the above embodiments, as an optional embodiment, the particle swarm optimization algorithm is obtained by introducing a compression factor to improve the velocity update method in the original particle swarm optimization algorithm; the compression factor is determined based on the algorithm's learning factor. Compared to the traditional particle swarm algorithm, the particle swarm optimization algorithm in this embodiment improves the algorithm's convergence efficiency by introducing a compression factor to control the system's final convergence.

[0071] In this embodiment, a discrete particle swarm optimization (PSO) algorithm based on a compression factor is provided. This algorithm is specifically designed for discrete problems. Through a reasonable discretization strategy, binary mapping can directly search the discrete solution space, avoiding the mapping errors of traditional PSO algorithms. It performs better in combinatorial optimization problems and utilizes constraint factors to control the final convergence of system behavior, further improving the algorithm's convergence speed. Furthermore, based on the algorithm's memory mechanism, it can dynamically track the current search status and adjust the search direction according to individual and global extrema, exhibiting good algorithmic complexity. The specific solution process is as follows: Step 1: Initialize the particle swarm with radar-mission node scheduling parameters as optimization variables, including swarm size. The position and velocity of each particle are expressed as follows: .

[0072] During the process, the real-time mission state of a single radar at each moment is treated as a particle to construct a... Local task assignment matrix As shown in equation (20). This matrix represents the matrix in... Global in real time Each network radar is for all The allocation status of each detection task. Indicates in Time Radar For exploration mission Radar-mission node scheduling parameters. It is a Boolean matrix composed of 0s and 1s, where each element is updated as a particle, and can be represented as: , (20); in, =1 means Real-time network radar Execute exploration mission ; =0 means Real-time network radar Will not perform exploration mission .

[0073] Step two: Based on the previously constructed multi-task collaborative planning model for airborne radar networking, determine the target fitness function of the particle swarm optimization algorithm. ,Right now .

[0074] Step 3: With the goal of maximizing the fitness value of the target fitness function, the particle swarm optimization algorithm is used to iteratively solve the problem using each particle in the particle swarm and the current detection distance parameter to obtain the globally optimal particle.

[0075] Specifically, this step includes the following sub-steps: Step S31: Substitute the current detection range parameter into the airborne radar network multi-task collaborative planning model to assign the range parameter value, and then filter the generated particles as shown in Equation (3). Determine whether the particles meet the algorithm constraints such as the maximum number of beams generated by a single radar node, the echo signal-to-noise ratio requirement of each radar node for the detection task, and the network radar joint interception probability requirement of the current task type.

[0076] Step S32, use the Sigmoid function As shown in equation (25), for the position of each particle Discretize the data.

[0077] Step S33: Calculate the fitness function for each particle that satisfies the constraints. fitness value For each particle, use its fitness value. and individual extreme values Comparison. If Then use Replace .

[0078] Then, for each particle Use its fitness value and global extrema Comparison. If Then use replace .

[0079] Step S34, iteratively update each particle speed and location In the discrete compressibility factor particle swarm algorithm Representing each particle The probability of taking the value 1 is updated as shown in equation (21), where, The introduced compression factor is represented by the formula shown in equation (21). As a learning factor, A random constant between 0 and 1. Position The update formula is shown in equation (24).

[0080] (twenty one); (twenty two) ;(twenty three) (twenty four) (25) Step S35: Determine if the algorithm termination condition (e.g., reaching the maximum number of iterations) is met. If yes, end the algorithm and output the optimization result to obtain the globally optimal particle; otherwise, return to step S32 above to continue iteratively solving.

[0081] The method in this application addresses the discrete problem in the multi-task collaborative planning model for airborne radar networking by combining a discrete particle swarm optimization algorithm with a compression factor. It employs a reasonable discretization strategy—using the sigmoid function to directly search the discrete solution space via binary mapping—fundamentally avoiding the errors caused by forced mapping in traditional particle swarm optimization algorithms, thus performing better in combinatorial optimization problems. Furthermore, the introduced compression factor regulates the final convergence of the system behavior, further accelerating the convergence speed. In addition, its built-in memory mechanism dynamically tracks the current search status and adjusts the search direction in a timely manner based on individual and global extrema, resulting in a relatively low overall algorithm complexity and further improving the execution efficiency of multi-task collaborative planning for airborne radar networking.

[0082] Furthermore, in the embodiments of this application, the aim is to study the multi-radar resource scheduling problem. To simplify the problem, it is assumed that the sampling interval of the airborne networked radar is... And the target and airborne radar If all are in uniform linear motion within a two-dimensional spatial domain, then Momentary Goal and radar The motion models can be represented as follows: (26); (27); In the formula, Indicate target The state transition matrix, express Momentary Goal The motion model, Indicates airborne radar The state transition matrix, express Airborne radar The motion model.

[0083] Since the target and the carrier aircraft are in a state of uniform motion, Momentary Goal and radar The state vectors can be represented as follows: (28); (29); in, and They represent Momentary Goal and radar Location, and They represent Momentary Goal and radar The speed of movement.

[0084] Furthermore, considering that the collaborative detection missions of long-range, offshore radar networks can be categorized into four types—target search, tracking, confirmation, and guidance—each corresponding to a specific radar operating mode, and given that each detection mission has different priorities, execution costs, and execution times in practical applications, this application proposes a mission attribute model to describe these characteristics. To meet the detection requirements of different missions, the detection mission model can be defined as follows: (30); In the formula, Indicates the first The airspace center location of each exploration mission Indicates the first The type of exploration mission, Indicates the first The weight of each detection task represents the priority of that task. Indicates the first The time cost of a single exploration mission Indicates the first The protection capability of a detection mission characterizes the safety assessment of that mission.

[0085] In the embodiments of this application, the motion model and detection task model constructed above are used to verify the advancement and feasibility of the algorithms in the aforementioned embodiments. The simulation scenario designed in this application is as follows: Considering that... N A radar network consisting of 5 airborne radars, designated Radar 1, Radar 2, Radar 3, Radar 4, and Radar 5, can generate a maximum of [number missing] simultaneous outputs from each radar. L =3 beams. The mission airspace is divided into 9 search intervals. Within each mission interval, there are 3 targets that require tracking, confirmation, and guidance tasks, respectively. Therefore, a total of 3 tasks need to be performed. M =18 missions. The radar cross-section (RCS) of each mission target relative to each radar is... Assume the radar radiation period Radiation time Pulse repetition period The pulse width is With fixed carrier frequency Launch, with a launch power of W, radar antenna gain is Intercept the receiver to measure frequency bandwidth ,search The frequency range, the receiver antenna gain is The receiver processor net gain is The noise figure is The false alarm probability is The echo signal-to-noise ratio requirements for radar performing four tasks. The simulation data consisted of 30 consecutive frames, with values ​​of 10, 20, 40, and 20 respectively. The weights of the four operating modes on the radar scheduling utility function are shown. The values ​​are 0.2, 0.3, 0.1, and 0.4, respectively.

[0086] The distribution of airborne radar sensors and the target trajectory set in this application are as follows: Figure 3 As shown; the search areas are divided and numbered as follows. Figure 4 As shown in the figure, the red triangle represents the search center of the area. The 18 tasks are numbered 1-18, where 1-9 represent searching for each search center, 10-12 represent tracking each target, 13-15 represent confirming each target, and 16-18 represent guiding each target.

[0087] Figures 5-9The task allocation results for radars 1-5 (sensors 1-5) are given respectively. It can be seen that the allocation results of radar-task node scheduling parameters are relatively stable and the task execution completion rate is relatively high in this scenario. The completion rate of high priority tasks, i.e., tasks with high radar scheduling utility weight, is relatively high, and the continuity of intelligence support can be effectively guaranteed.

[0088] At the beginning of the simulation, the radars start from their respective initial areas. Radar 1 primarily searches for mission areas 1, 2, and 4, which are relatively close, executing tasks 1, 2, and 4. Similarly, radars 2-5 execute search tasks 3, 5, 6, and 7, which are closer to their own centers compared to these areas, thus maximizing the efficiency of multi-task collaborative execution. Furthermore, as the simulation progresses, radar 2 approaches mission area 7, i.e., mission target 1, and allocates time resources to this area for tracking and guiding tasks 10 and 16.

[0089] It is worth noting that although target 2 is spatially closer to radar 2, the higher task priority results in a higher task weight for tracking and confirming target 1. Therefore, from the perspective of overall efficiency, tasks 10 and 16 are assigned to radar 2, while the tracking and guidance of target 1 is handled by radar 3, which performs tasks 11 and 17. Similarly, radar 4 performs the search of area 9, i.e., task 9, with higher efficiency. This approach better achieves the optimization goal of maximizing the global utility function of the airborne network radar collaborative detection task allocation, and realizes a more efficient, rational, and dynamic allocation and scheduling of airborne early warning resources in the far sea and far-field areas.

[0090] To better demonstrate the superiority of the proposed algorithm in global task allocation, this application, while keeping other radar parameters unchanged, compares the collaborative detection performance of airborne networked radars under existing uniform allocation and random allocation algorithms, analyzing the performance differences of different task allocation algorithms. The two comparison algorithms are described in detail below: (1) Uniform Allocation Algorithm: While keeping the radar parameters the same, the weight coefficients of the task-radar node allocation are evenly distributed, and the detection performance of the airborne network radar is evaluated through the task performance function. (2) Random allocation algorithm: While keeping the radar parameters the same, the weight coefficients of the task-radar node allocation are randomly allocated, and the detection performance of the airborne network radar is evaluated through the task performance function. Figure 10 The results shown are a comparison of different algorithms. Figure 10As can be seen from the simulation, the objective function value of the method proposed in this application is much larger than that of other methods. The objective function value in the simulation shows a significant improvement compared to other algorithms, proving that this method achieves efficient allocation of radar and tasks and can significantly improve the collaborative detection efficiency of multi-aircraft radars. By constructing a global task allocation utility function, and comprehensively considering multi-dimensional constraints such as radar environment survivability, task priority, radar-target distance, and echo signal-to-noise ratio, dynamic matching of radar resources and tasks is achieved, significantly improving the collaborative efficiency of multi-tasks. This algorithm can handle dynamic scenarios such as uniform radar motion and diversified tasks (search, tracking, confirmation, guidance), and adjusts the resource allocation strategy in real time by iteratively updating particle positions and velocities.

[0091] To verify the superior performance of the Discrete Compression Factor Particle Swarm Optimization (PCO) algorithm proposed in this application, performance analyses were conducted on the PCO, Discrete Particle Swarm Optimization (DPSO), Classical Particle Swarm Optimization (PSO), and Three-Particle Swarm Optimization (TPSO) algorithms, respectively, under the condition that the radar parameters remain unchanged.

[0092] like Figure 11 As shown, under the same number of iterations, the Discrete Compressibility Factor Particle Swarm Optimization (DCO) algorithm exhibits faster convergence speed and higher model solution accuracy. By improving upon the traditional Particle Swarm Optimization (PSO) algorithm, the DCO algorithm addresses the core challenges of discrete optimization problems while retaining the advantages of swarm intelligence, effectively enhancing algorithm performance.

[0093] The multi-task collaborative planning method for airborne radar networking proposed in this application adaptively optimizes the radar-task node scheduling parameters to improve the overall execution efficiency of multiple tasks. Simulation experiments show that the proposed algorithm can effectively improve the overall execution efficiency of multiple tasks and achieve efficient collaborative detection of airborne networked radars. Furthermore, due to the complexity and variability of the modern environment, new requirements have been placed on the management of radar radio frequency resources. Therefore, in the context of multi-task collaborative detection planning, further consideration of optimizing the allocation of radio frequency resources is also an important research direction for the future.

[0094] The airborne radar networking multi-task collaborative planning device provided in this application is described below. The airborne radar networking multi-task collaborative planning device described below and the airborne radar networking multi-task collaborative planning method described above can be referred to in correspondence.

[0095] Figure 12 This is a schematic diagram of the structure of the airborne radar networking multi-task collaborative planning device provided in the embodiments of this application, as shown below. Figure 12 As shown, it includes: Model calling module 10 is used to call the constructed airborne radar network multi-task collaborative planning model. The airborne radar network multi-task collaborative planning model is established with the goal of maximizing the collaborative execution efficiency of airborne radar tasks, based on constraints such as the joint interception probability of the networked radars, the signal-to-noise ratio of the mission echo, and the system detection performance. The joint interception probability of the networked radars is determined based on the interception probability when the window functions of the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain. The model solving module 20 is used to solve the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network, and to determine the current task allocation results of each network radar.

[0096] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.

[0097] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0098] The airborne radar networking multi-task collaborative planning device of this application embodiment considers the significant impact of radar survivability in far-sea and far-field environments on the overall execution efficiency of the airborne radar networking system. It introduces the joint interception probability of multi-task networking radars as an evaluation index to measure the stealth capability of a single radar from four dimensions: power domain, time domain, spatial domain, and frequency domain. Combined with the mission echo signal-to-noise ratio and system detection performance as constraints, an airborne radar networking multi-task collaborative planning model is established with the goal of maximizing the collaborative execution efficiency of airborne radar missions. It adaptively optimizes the allocation of multiple tasks for multiple networking radars, which can effectively realize multi-task collaborative planning of multiple radars. At the same time, it can also effectively improve the execution efficiency of radar networking multi-task collaboration and achieve efficient resource scheduling of airborne radars.

[0099] Based on the methods in the above embodiments, this application provides an electronic device, such as... Figure 13 As shown, the electronic device may include a processor 1310, a communications interface 1320, a memory 1330, and a communication bus 1340, wherein the processor 1310, the communications interface 1320, and the memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logical instructions in the memory 1330 to execute the methods in the above embodiments.

[0100] Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0101] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0102] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0103] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0104] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0105] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0106] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0107] It should be understood that expressions such as “comprising” and “may include” used in this application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In this application, terms such as “comprising” and / or “having” are to be interpreted as indicating a particular characteristic, number, operation, constituent element, component, or combination thereof, but not to exclude the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application 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 this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An airborne radar networking multi-mission cooperative planning method, characterized in that, include: The pre-constructed airborne radar network multi-task collaborative planning model is invoked. This model is established to maximize the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the mission echo, and the system detection performance. The joint interception probability of the networked radars is determined based on the interception probability when the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain window functions. The detection range parameters of the current airborne radar network are used to solve the multi-task collaborative planning model of the airborne radar network to determine the current task allocation results of each network radar. The joint interception probability of the networked radars is determined through the following steps: Based on the detection probability model of the intercept receiver for radar radiated signals, the first probability of the radiated signals of each of the networked radars being intercepted in the power domain and the corresponding average window width are determined. Based on the radar radiation model, the second probability of the radiation signal of each of the networked radars being intercepted in the time domain and the corresponding average window width are determined. Based on the state of the radar signal pointing to the intercept receiver of each of the networked radars, the third probability of the radiated signal of each of the networked radars being intercepted in the airspace and the corresponding average window width are determined. Based on the detection parameters of the intercept receiver and the radar operating frequency of each of the network radars, the fourth probability of the radiated signal of each of the network radars being intercepted in the frequency domain and the corresponding average window width are determined. Based on each of the first probabilities and their corresponding average window widths, each of the second probabilities and their corresponding average window widths, each of the third probabilities and their corresponding average window widths, and each of the fourth probabilities and their corresponding average window widths, the interception probability of each network radar when the window functions in the power domain, time domain, spatial domain, and frequency domain simultaneously overlap is determined, and the joint interception probability of the network radar is determined according to the interception probability corresponding to each network radar.

2. The airborne radar networking multi-task cooperative planning method according to claim 1, characterized in that, The method for determining the first probability of interception of the radiated signal of each network radar in the power domain and the corresponding average window width based on the detection probability model of the intercepting receiver for radar radiated signals includes: Obtain the signal-to-noise ratio of each of the networked radars detected by the intercept receiver; By inputting the given false alarm probability and each of the networked radars into the detection probability model, a first probability is obtained that the radiated signal of each of the networked radars is intercepted in the power domain. Based on the first probability and the corresponding radiation period of each of the networked radars, the average window width at which the radiated signal of each of the networked radars is intercepted in the power domain is determined.

3. The airborne radar networking multi-task collaborative planning method according to claim 1, characterized in that, The determination of the second probability of the radiated signal of each of the networked radars being intercepted in the time domain and the corresponding average window width based on the radar radiation model includes: Based on the radar radiation model, the radiation time of each network radar to the intercept receiver, the radiation period, pulse width and pulse repetition period of each network radar are determined. Based on the radiation time of each network radar to the intercepting receiver, the pulse width and pulse repetition period of each network radar, the average window width of the radiation signal of each network radar intercepted in the time domain is determined. By calculating the ratio of the average window width to the corresponding radiation period for each of the networked radars, the second probability of the radiation signal of each of the networked radars being intercepted in the time domain is determined.

4. The airborne radar networking multi-task cooperative planning method according to claim 1, characterized in that, The detection parameters include instantaneous bandwidth and detection frequency band; the determination of the fourth probability of interception of the radiated signal of each network radar in the frequency domain and the corresponding average window width based on the detection parameters of the intercepting receiver and the radar operating frequency of each network radar includes: Determine the ratio of the instantaneous bandwidth of the intercepting receiver to the detection frequency band; Determine the product of the ratio and the radar operating frequency of each of the networked radars; Based on the product of each of the networked radars, the fourth probability of the radiated signal of each of the networked radars being intercepted in the frequency domain is obtained. Based on the fourth probability and the corresponding radiation period of each of the networked radars, the average window width at which the radiated signal of each of the networked radars is intercepted in the frequency domain is determined.

5. The airborne radar networking multi-task cooperative planning method according to any one of claims 1-4, characterized in that, The process of solving the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network to determine the current task allocation results for each network radar includes: Initialize the particle swarm with radar-mission node scheduling parameters as optimization variables; Based on the aforementioned airborne radar networking multi-task collaborative planning model, the target fitness function of the particle swarm optimization algorithm is determined. With the goal of maximizing the fitness value of the target fitness function, the particle swarm optimization algorithm is iteratively solved using the particle swarm and the detection distance parameter to obtain the globally optimal particle. Based on the globally optimal particle, the optimal solution corresponding to the radar-task node scheduling parameters is obtained; The current task allocation result of each network radar is obtained based on the optimal solution corresponding to the radar-task node scheduling parameters.

6. The airborne radar networking multi-task cooperative planning method according to claim 5, characterized in that, The particle swarm optimization algorithm is obtained by improving the velocity update method in the original particle swarm optimization algorithm by introducing a compression factor; the compression factor is determined based on the algorithm learning factor.

7. A multi-task collaborative planning device for airborne radar networking, characterized in that, include: The model invocation module is used to invoke the constructed airborne radar network multi-task collaborative planning model. This model is established to maximize the collaborative execution efficiency of airborne radar tasks, constrained by the joint interception probability of the networked radars, the signal-to-noise ratio of the mission echo, and the system detection performance. The joint interception probability of the networked radars is determined based on the interception probability when the radiated signals of each networked radar simultaneously overlap in the power domain, time domain, spatial domain, and frequency domain window functions. The model solving module is used to solve the multi-task collaborative planning model of the airborne radar network using the detection range parameters of the current airborne radar network, and to determine the current task allocation results of each network radar. The joint interception probability of the networked radars is determined through the following steps: Based on the detection probability model of the intercept receiver for radar radiated signals, the first probability of the radiated signals of each of the networked radars being intercepted in the power domain and the corresponding average window width are determined. Based on the radar radiation model, the second probability of the radiation signal of each of the networked radars being intercepted in the time domain and the corresponding average window width are determined. Based on the state of the radar signal pointing to the intercept receiver of each of the networked radars, the third probability of the radiated signal of each of the networked radars being intercepted in the airspace and the corresponding average window width are determined. Based on the detection parameters of the intercept receiver and the radar operating frequency of each of the network radars, the fourth probability of the radiated signal of each of the network radars being intercepted in the frequency domain and the corresponding average window width are determined. Based on each of the first probabilities and their corresponding average window widths, each of the second probabilities and their corresponding average window widths, each of the third probabilities and their corresponding average window widths, and each of the fourth probabilities and their corresponding average window widths, the interception probability of each network radar when the window functions in the power domain, time domain, spatial domain, and frequency domain simultaneously overlap is determined, and the joint interception probability of the network radar is determined according to the interception probability corresponding to each network radar.

8. An electronic device, characterized in that, Includes memory and one or more processors; The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions; The one or more processors invoke the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-6.

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