Optimization of radar reconnaissance constellation based on fuzzy relative entropy

Yali Liu; Wei Xiong; Chi Han

doi:10.1117/12.3004590

16 August 2023 Optimization of radar reconnaissance constellation based on fuzzy relative entropy

Yali Liu, Wei Xiong, Chi Han

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870J (2023) https://doi.org/10.1117/12.3004590
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The optimization of radar reconnaissance constellation is of great significance for reconnaissance timeliness. In order to solve the problems of insufficient selection pressure and poor diversity of the evolutionary algorithm based on Pareto domination in solving the many-objective optimization problem of radar reconnaissance constellation, an imporved particle swarm optimization based on fuzzy relative entropy (IFREM_PSO) is proposed. A new inertial weight strategy is proposed to enhance the convergence speed and accuracy of the algorithm, improve the external archive maintenance strategy, and enhance the convergence and diversity of the algorithm. The optimization model of radar imaging reconnaissance constellation oriented to area reconnaissance is constructed. The IFREM_PSO is used to optimize the model, and the multi-objective particle swarm optimization algorithm (MOPSO) is used as the control algorithm. Experiments show that the IFREM_PSO has better results in terms of convergence, diversity and convergence speed, and can effectively solve the problem of radar reconnaissance constellation optimization.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yali Liu, Wei Xiong, and Chi Han "Optimization of radar reconnaissance constellation based on fuzzy relative entropy", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870J (16 August 2023); https://doi.org/10.1117/12.3004590

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