Distributed Blanket Jamming Resource Scheduling for Satellite Navigation Based on Particle Swarm Optimization and Genetic Algorithm

Luo, Zhaoyi; Deng, Min; Yao, Zhiqiang; Leng, Xiaofan; Chen, Yongqi

doi:10.1109/icct50939.2020.9295811

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…However, such methods only consider two optimization objectives, making it difficult to directly obtain the optimal solution when there are more objective functions. In [34], a satellite navigation distributed blanket jamming resource scheduling method based on particle swarm optimization and a genetic algorithm was proposed, in which the objectives of bit error rate, pseudorange measurement error, and jamming cost were optimized. This method effectively scheduled limited resources.…”

Section: Related Workmentioning

confidence: 99%

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

Tang,

Ding,

Zhang

2024

Remote Sensing

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Due to their low orbit, low-Earth-orbit (LEO) satellites possess advantages such as minimal transmission delay, low link loss, flexible deployment, diverse application scenarios, and low manufacturing costs. Moreover, by increasing the number of satellites, the system capacity can be enhanced, making them the core of future communication systems. However, there have been instances where malicious actors used LEO satellite communication equipment to illegally broadcast events in large sports stadiums or engage in unauthorized leakage of military secrets in sensitive military areas. This has become an urgent issue in the field of communication security. To combat and prevent abnormal and illegal communication activities using LEO satellites, this study proposes a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm. Firstly, a distributed jamming system model for the LEO satellite downlink is established. Then, using a non-dominated sorting genetic algorithm, the jamming parameters are optimized in the power, time, and frequency domains. Field jamming experiments were conducted in the southwest outskirts of Xi’an, China, targeting the LEO constellation of the China Satellite Network. The results indicate that under the condition that the jamming coverage rate is no less than 90%, the proposed method maximizes jamming power, minimizes time delay, and minimizes frequency compensation compared to existing jamming optimization methods, effectively improving the real-time jamming performance and success rate.

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Section: Related Workmentioning

confidence: 99%

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

Tang,

Ding,

Zhang

2024

Remote Sensing

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“…These aspects have made it difficult to counter massive UAVs. With the rapid development of neural networks, an increasing number of works use heuristic algorithms [23,24] and exact algorithms [25,26] to solve the distributed optimization jamming problem to address the huge amount of computation brought by the changeable airspace environment. The work reported in [23] proposed an IAC algorithm to optimize the allocation of distributed jamming sources, which speeds up the search and optimization.…”

Section: Related Workmentioning

confidence: 99%

“…The work reported in [23] proposed an IAC algorithm to optimize the allocation of distributed jamming sources, which speeds up the search and optimization. In [24], the PSO algorithm and genetic algorithm are applied to the distributed jamming source allocation model, which effectively improves the jamming benefit. In [25], Chen et al established a 0-1 integer linear programming model to solve the non-linear combinatorial optimization problem of jamming sources.…”

Section: Related Workmentioning

confidence: 99%

Distributed massive UAV jamming optimization algorithm with artificial bee colony

Chen

2022

IET Communications

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With the massive application of unmanned aerial vehicles (UAVs) in disaster relief, fire protection, security and other fields, jamming countermeasures to UAVs in airports and competition venues have become the core link of security in high‐level matches and important places. The existing jamming sources are mainly single point jamming. Due to the shielding of buildings, it is impossible to fully cover the venue, and the existing suppressed jamming will affect the communication broadcasting. Here, a distributed optimal jamming method for massive UAVs is proposed. Through the identification and positioning of massive UAVs, using the location information of distributed jamming sources, the UAV jamming architecture based on satellite navigation is constructed. Combined with tabu search artificial bee colony theory, the distributed optimal jamming of UAVs is realized, which solves the problems of few jamming targets, slow speed and low success rate of the existing optimal jamming methods. The proposed method is compared with the existing methods in three aspects: communication frequency, jamming trajectory deviation and optimization speed. The simulation results show that the proposed method effectively improves the jamming success rate and jamming speed and can meet the application requirements of massive UAV jamming in the region.

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“…In the multi-jammer cooperative jamming technology, the jammer should quickly form an effective jamming resource allocation or a decision-making method according to the battlefield environment, jamming resources, and tactical requirements. In this field, research contributions can be mainly divided into two categories of single-beam [9][10][11][12][13][14] and multibeam [6][7][8] jamming systems. Multi-beam jamming systems can generate multiple beams to jam different radar nodes, with the relevant research summarised as follows.…”

Section: Introductionmentioning

confidence: 99%

Combination search strategy‐based improved particle swarm optimisation for resource allocation of multiple jammers for jamming netted radar system

Zou

Niu

Liu

et al. 2023

IET Signal Processing

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This paper presents a new algorithm named combination search strategy (CSS)‐based improved particle swarm optimisation (IPSO) to address the resource allocation problem of multiple jammers. First, the authors propose a CSS to effectively broaden the limited search range of the two‐step solving framework. This method not only simplifies the solution framework but also considers the combined relationship between beam pointing and transmit power to determine the global solution to the original problem directly. Second, the authors propose the IPSO because the complexity of the decision variables is increased by CSS. This method can change the focus of searching the optimal solution at different stages, correct the direction of particle evolution over time and avoid the interference between the variables. Finally, this study simulates the problem of resource allocation of multiple jammers based on the CSS‐IPSO. Based on the simulation results, the combined search strategy can obtain better resource allocation results in a short time, and the IPSO algorithm can further improve the accuracy and stability of the resource allocation results.

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Distributed Blanket Jamming Resource Scheduling for Satellite Navigation Based on Particle Swarm Optimization and Genetic Algorithm

Cited by 6 publications

References 8 publications

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

Distributed massive UAV jamming optimization algorithm with artificial bee colony

Combination search strategy‐based improved particle swarm optimisation for resource allocation of multiple jammers for jamming netted radar system

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