Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

Lu, Dejiang; Wang, Xing; Wu, Xiaotian; Chen, You

doi:10.1016/j.dt.2022.04.013

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Zou [23], Wu [24], Jiang [25], and others have made improvements to the PSO algorithm to enhance its optimization probability and stability when dealing with cooperative jamming resource allocation. Lu et al [26] employed detection and targeting probability to characterize the jamming effectiveness of a networked radar system in search and tracking modes, constructed a dual-factor jamming effectiveness assessment function, established a non-convex optimization model for cooperative jamming in a networked radar system, and utilized the dynamic adaptive discrete cuckoo search algorithm (DADCS), featuring improved path update strategies and the introduction of a global learning mechanism, to solve the model. This approach achieved the cooperative jamming resource allocation for aircraft formation in a networked radar system.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

Xin,

Chen

2024

Remote Sensing

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Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness.

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Section: Introductionmentioning

confidence: 99%

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

Xin,

Chen

2024

Remote Sensing

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“…Using the probability of grouped radar detection as an evaluation index, Jiang et al [11] calculated the corresponding cooperative jamming effectiveness based on the different spatial positions of the formation aircraft and its jamming decision scheme. However, jamming resource allocation as an NP-Hard problem [12] will take a lot of time to solve the allocation scheme for each point.…”

Section: Introductionmentioning

confidence: 99%

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM

Yang,

Wang,

Cheng

et al. 2024

Preprint

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Cooperative jamming effectiveness evaluation is a key component in completing the cooperative jamming OODA loop. For the problem of evaluating the effectiveness of cooperative jamming to group network radar by formation aircraft, a cooperative jamming effectiveness evaluation method based on Improved Particle Swarm Optimization- Extreme Learning Machine (IPSO-ELM) is proposed. Firstly, based on the working parameters of the group network radar and the information fusion rules, the cooperative jamming effectiveness evaluation function is established. On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on Extreme Learning Machine (ELM) methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. This optimization aims to boost the model’s predictive precision. Finally, the IPSO-ELM algorithm is subjected to rigorous assessment via simulation, confirming its performance of accuracy and efficiency. From the simulation results, the advanced performance of the IPSO-ELM algorithm, specifically in the context of assessing the effectiveness of cooperative jamming is verified.

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“…The rank K judgement criterion is used to calculate the fusion detection probability of radar network [5–8], which indicates that when there are K or more than K radars in the net discovering target at the same time, the netted radar system determines that the target is found. The detailed calculation formula of the fusion detection probability can be referred to [8], the computation is simply expressed as Equation () in this letter.

\begin{equation}P_{{d}_{net}}^k = K(P_{{d}_n}^k)\end{equation}

…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation of jamming effect is the basis of resource allocation model, [1][2][3][4] used the form of membership function to evaluate the jamming effectiveness. Detection probability and aiming probability which reflect the detection and tracking capability of netted radar are used respectively in [5][6][7] as evaluation functions of jamming effect, the two are both considered in [8] to establish a jamming resource allocation model.…”

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confidence: 99%

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Cooperative jamming resource allocation model and algorithm for netted radar

Yao

Tang

Wang

et al. 2022

Electronics Letters

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In this letter, the jamming resource allocation problem of distributed jammers cooperatively jamming netted radar system is investigated. A well‐constructed jamming resource allocation model considering jamming beams, jamming power and other influencing factors is established. Random keys are used in this letter to improve the coding mode of genetic algorithm. Simulation results show that in the case of limited jamming resources, the model and algorithm proposed can achieve effective jamming allocation schemes facing a netted radar with any number of radar nodes.

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Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

Cited by 8 publications

References 15 publications

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM

Cooperative jamming resource allocation model and algorithm for netted radar

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