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

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (3): 834-840.doi: 10.12305/j.issn.1001-506X.2022.03.16

基于进化PSO算法的稀疏捷变频雷达波形优化

杜思予^1,*, 全英汇¹, 沙明辉², 方文¹, 邢孟道³

1. 西安电子科技大学电子工程学院, 陕西西安 710071
2. 北京无线电测量研究所, 北京 100854
3. 西安电子科技大学雷达信号处理国家重点实验室, 陕西西安 710071

收稿日期:2020-12-31 出版日期:2022-03-01 发布日期:2022-03-10
通讯作者: 杜思予
作者简介:杜思予(1998—), 女, 硕士研究生, 主要研究方向为雷达信号处理|全英汇(1981—), 男, 教授, 博士, 主要研究方向为雷达实时信号处理|沙明辉(1986—), 男, 博士, 主要研究方向为雷达抗干扰和信号处理|方文(1997—), 男, 硕士研究生, 主要研究方向为雷达信号处理|邢孟道(1975—), 男, 教授, 博士, 主要研究方向为SAR/ISAR成像、动目标检测
基金资助:
国家自然科学基金(61772397);国家自然科学基金(61303035)

Waveform optimization for SFA radar based on evolutionary particle swarm optimization

Siyu DU^1,*, Yinghui QUAN¹, Minghui SHA², Wen FANG¹, Mengdao XING³

1. School of Electronic Engineering, Xidian University, Xi'an 710071, China
2. Beijing Institute Radio Measurement, Beijing 100854, China
3. National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

Received:2020-12-31 Online:2022-03-01 Published:2022-03-10
Contact: Siyu DU

摘要/Abstract

摘要：

为了提高稀疏捷变频(sparse frequency agility, SFA)雷达信号在稀疏重构中的精度和稳定性, 提出一种基于进化粒子群优化(particle swarm optimization, PSO)算法的SFA雷达信号的优化设计。首先, 推导了SFA雷达的信号模型和稀疏重构时的字典矩阵。然后, 以最小化SFA雷达信号对应字典矩阵的相关性为目标函数, 稀疏载频情况下有效带宽和有效跳频间隔为约束条件, 建立优化模型。最后, 通过进化PSO算法求解得到最优载频序列。仿真结果表明, 所提算法在满足稀疏性约束的条件下, 能够有效提高字典矩阵的正交性, 保证稀疏重构信号的精度和可靠性。

关键词: 波形优化, 稀疏捷变频雷达, 稀疏重构, 进化粒子群优化算法

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

中图分类号:

TN958.6

杜思予, 全英汇, 沙明辉, 方文, 邢孟道. 基于进化PSO算法的稀疏捷变频雷达波形优化[J]. 系统工程与电子技术, 2022, 44(3): 834-840.

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.

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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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基于进化PSO算法的稀疏捷变频雷达波形优化

Waveform optimization for SFA radar based on evolutionary particle swarm optimization

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