Waveform optimization for SFA radar based on evolutionary particle swarm optimization

被引：0
|
作者：
Du S. [1 ]
Quan Y. [1 ]
Sha M. [2 ]
Fang W. [1 ]
Xing M. [3 ]
机构：
[1] School of Electronic Engineering, Xidian University, Xi'an
[2] Beijing Institute Radio Measurement, Beijing
[3] National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 03期
关键词：
Evolutionary particle swarm optimization (PSO) algorithm; Sparse frequency agility (SFA) radar; Sparse reconstruction; Waveform optimization;
D O I：
10.12305/j.issn.1001-506X.2022.03.16
中图分类号：
学科分类号：
摘要：
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. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：834 / 840
页数：6
相关论文
共 24 条
  • [1] MISHRA K V, MULLETI S, ELDAR Y C., RaSSteR: random sparse step-frequency radar
  • [2] QUAN Y H, WU Y J, LI Y C, Et al., Range-Doppler reconstruction for frequency agile and PRF-jittering radar, IET Radar, Sonar & Navigation, 12, 3, pp. 348-352, (2018)
  • [3] HUANG T Y., Coherent frequency-agile radar signal processing by solving an inverse problem with a sparsity constraint, (2014)
  • [4] WU Y J., Research on anti-jamming performance of frequency agility radar, (2018)
  • [5] ZHANG Y X, DIAO M., Waveform adaptive design technology based on particle swarm optimization, Applied Science and Technology, 46, 5, pp. 39-44, (2019)
  • [6] CHEN H G, WU J S, WANG J L, Et al., Mechanism study of simulated annealing algorithm, Journal of Tongji University, 32, 6, pp. 802-805, (2004)
  • [7] WU Y., Anti-jamming technology of cognitive radar based on adaptive agile waveform, (2018)
  • [8] CHEN B X., Modern radar system analysis and design, (2012)
  • [9] YAO H B., Research on the anti-interference performance of multiple parameter-agility radar, (2019)
  • [10] HUANG T, LIU Y, MENG H, Et al., Cognitive random stepped frequency radar with sparse recovery, IEEE Trans.on Aerospace and Electronic Systems, 50, 2, pp. 858-870, (2014)
123