Joint Jamming Beam and Power Scheduling for Suppressing Netted Radar System

Zhang, Dalin; Sun, Jun; Yi, Wei; Yang, Chou-Chen; Wei, Yaqi

doi:10.1109/radarconf2147009.2021.9455337

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…In a study conducted by Jiang et al 23 , a hybrid quantum-behaved particle swarm optimization and self-adjustable genetic algorithm (HQPSOGA) was employed to solve jamming resource allocation, which effectively generated a better overall jamming capacity. Zhang et al 24 used particle swarm optimization (PSO) to solve the optimization problem transformed by joint jamming beam selection and power allocation (JJBSPA), which veri ed the JJBSPA strategy.…”

Section: Introductionmentioning

confidence: 99%

A Study of Jamming Resource Allocation Based on a Hyperheuristic Framework

Hao

Wang

2022

Preprint

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There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. To solve this problem, this study proposes an improved genetic selection electronic warfare operator hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the hyperheuristic (HH) framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four electronic warfare operators (EWOs) based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

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Section: Introductionmentioning

confidence: 99%

A Study of Jamming Resource Allocation Based on a Hyperheuristic Framework

Hao

Wang

2022

Preprint

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“…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.…”

mentioning

confidence: 99%

“…Many heuristic algorithms have been improved to solve the problem, such as the firefly algorithm [1], genetic algorithm [2], particle swarm algorithm [3,[5][6][7] and cuckoo algorithm [8].…”

mentioning

confidence: 99%

“…where P fa represents the false alarm probability of the radar, I denotes the number of non-coherent accumulated pulses. The rank K judgement criterion is used to calculate the fusion detection probability of radar network [5][6][7][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 (7) in this letter.…”

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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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