基于进化PSO算法的稀疏捷变频雷达波形优化

doi:10.12305/j.issn.1001-506X.2022.03.16

Abstract

Abstract:

To enhance the accuracy and stability of sparse frequency agility (SFA) radar signal in sparse reconstruction, an optimization design of SFA radar is proposed, which is based on evolutionary particle swarm optimization (PSO) algorithm. Firstly, the signal model of SFA radar and the dictionary matrix during sparse reconstruction are derived. Then, the optimization model is constructed with objective function of the correlation of the dictionary matrix minimization and the constraint conditions of the effective bandwidth and the effective frequency agility interval. Finally, the optimal carrier frequency solution is obtained by evolutionary PSO algorithm. Simulation results show that the proposed algorithm can effectively improve the orthogonality of the measurement matrix to ensure the accuracy and reliability of the signal sparse reconstruction under the condition of sparse constraint.

Key words: waveform optimization, sparse frequency agility (SFA) radar, sparse reconstruction, evolutionary particle swarm optimization (PSO) algorithm

CLC Number:

TN958.6

Siyu DU, Yinghui QUAN, Minghui SHA, Wen FANG, Mengdao XING. Waveform optimization for SFA radar based on evolutionary particle swarm optimization[J]. Systems Engineering and Electronics, 2022, 44(3): 834-840.

Figures/Tables 10

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References 24

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算法	迭代次数/次	单次迭代平均运行时间/s	最优目标函数值
文献[4]	461	5.762	0.524 6
PSO	7	2.731	0.563 7
本文	41	2.672	0.527 5

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Waveform optimization for SFA radar based on evolutionary particle swarm optimization

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