Cooperative jamming resource allocation model and algorithm for netted radar

Yao, Zekun; Tang, Chuanbin; Wang, Chao; Shi, Qinzhan; Yuan, Naichang

doi:10.1049/ell2.12611

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Xing et al [27] evaluated jamming suppression effects using a constant false-alarm rate detection model for networked radar, established a cooperative control model for multiple jammers involving jamming beams and transmission power levels, and utilized an enhanced improved artificial bee colony (IABC) algorithm to derive optimized allocation solutions for different operational scenarios. Yao et al [28] developed a jamming resource allocation model considering factors such as jamming beams and jamming power, and solved the model using an improved genetic algorithm (GA), allowing for jamming resource allocation in networked radar systems with any number of nodes under conditions of limited jamming resources. Xing et al [29] quantified target threat levels through fuzzy comprehensive evaluation, constructed a jamming resource allocation model based on the jamming efficiency matrix, and proposed an improved firefly algorithm (FA) to solve the model, ensuring both the rationality of task assignments and the stability of the algorithm.…”

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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An interference power allocation method against multi-objective radars based on optimized proximal policy optimization

Zhang,

Wang,

Zhou

et al. 2025

Signal Processing

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Research on adaptive scheduling strategy of cooperative jamming resources for the anti-netted radar system

Yang,

Wang,

Cheng

et al. 2024

AIP Advances

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Jamming resource allocation is an important step in implementing cooperative jamming. Traditional jamming resource allocation considers only the reduction of enemy radar detection performance but ignores the problem of mutual interference in the formation. Therefore, an adaptive scheduling strategy based on the Improved Sparrow Search Algorithm (ISSA) is proposed to improve the jamming efficiency and minimize mutual interference. First, the target detection model and the mutual interference model are established. On this basis, a two-factor objective function based on network detection probability and the degree of mutual interference is constructed. Then, a whole-process cooperative mechanism is proposed, and an improved circle algorithm and a whale predation strategy are introduced. These strategies are used to overcome the problems of slow convergence and local optimization in the standard sparrow search algorithm (SSA). Finally, the effectiveness and timeliness of ISSA are proved by simulation experiments.

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Cooperative jamming resource allocation model and algorithm for netted radar

Cited by 6 publications

References 9 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

An interference power allocation method against multi-objective radars based on optimized proximal policy optimization

Research on adaptive scheduling strategy of cooperative jamming resources for the anti-netted radar system

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