A Joint Allocation Method of Multi-Jammer Cooperative Jamming Resources Based on Suppression Effectiveness

Xing, Huaixi; Qing-hua, Xing; Wang, Kun

doi:10.3390/math11040826

Cited by 7 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach achieved the cooperative jamming resource allocation for aircraft formation in a networked radar system. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Xin,

Chen

2024

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%