基于改进瞪羚优化的被动合成孔径快速定位算法

doi:10.12305/j.issn.1001-506X.2026.08.04

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (8): 2535-2544.doi: 10.12305/j.issn.1001-506X.2026.08.04

• 电子技术 • 上一篇

基于改进瞪羚优化的被动合成孔径快速定位算法

王璐(), 王轩(), 吴仁彪()

中国民航大学天津市智能信号与图像处理重点实验室，天津 300300

收稿日期:2025-05-12 修回日期:2025-06-24 接受日期:2025-07-05 出版日期:2026-06-01 发布日期:2026-06-01
通讯作者: 吴仁彪 E-mail:13920072930@163.com;17614936593@163.com;rbwu@cauc.edu.cn
作者简介:王　璐（1984—），女，讲师，硕士，主要研究方向为卫星导航信号处理、阵列信号处理
王　轩（2000—），男，硕士研究生，主要研究方向为被动合成孔径、阵列信号处理
基金资助:
国家自然科学基金（U2133204）资助课题

Fast localization algorithm for passive synthetic aperture based on improved gazelle optimization

Lu WANG(), Xuan WANG(), Renbiao WU()

Tianjin Key Laboratory for Advanced Signal Processing，Civil Aviation University of China，Tianjin 300300，China

Received:2025-05-12 Revised:2025-06-24 Accepted:2025-07-05 Online:2026-06-01 Published:2026-06-01
Contact: Renbiao WU E-mail:13920072930@163.com;17614936593@163.com;rbwu@cauc.edu.cn

摘要/Abstract

摘要：

针对现有被动合成孔径（passive synthetic aperture，PSA）定位算法采用穷尽搜索策略导致计算复杂度较高的问题，提出一种融合改进瞪羚优化算法（improved gazelle optimization algorithm，IGOA）与后向投影（back projection，BP）方法的PSA快速定位算法，该算法首先基于合成孔径雷达（synthetic aperture radar，SAR）中的BP成像原理构建PSA定位代价函数；随后设计瞪羚优化算法（gazelle optimization algorithm，GOA）的改进策略，以提升全局搜索效率与收敛精度；最后基于IGOA对BP代价函数寻优实现目标源定位。仿真实验验证了所提算法在收敛速度、定位精度与计算复杂度方面的优越性能。结果表明所提算法能够实现高精度快速定位。

关键词: 无源定位, 被动合成孔径, 快速定位算法, 瞪羚优化算法

Abstract:

To address the issue of high computational complexity caused by the exhaustive search strategy used in existing passive synthetic aperture (PSA) localization methods, this paper proposes a fast PSA localization algorithm that integrates an improved gazelle optimization algorithm （IGOA） with the back projection （BP） method. First, the algorithm constructs a PSA localization cost function based on the BP imaging principle in synthetic aperture radar （SAR）. Then, an improved strategy for the gazelle optimization algorithm （GOA） is designed to enhance global search efficiency and convergence accuracy. Finally, the optimal target localization is obtained by the BP cost function using IGOA. Simulation results verify that the proposed algorithm outperforms existing methods in terms of convergence speed, localization accuracy, and computational complexity. The results demonstrate that the proposed method achieves fast and high-precision localization.

Key words: passive localization, passive synthetic aperture, fast localization algorithm, gazelle optimization algorithm

中图分类号:

TN 973

王璐, 王轩, 吴仁彪. 基于改进瞪羚优化的被动合成孔径快速定位算法[J]. 系统工程与电子技术, 2026, 48(8): 2535-2544.

Lu WANG, Xuan WANG, Renbiao WU. Fast localization algorithm for passive synthetic aperture based on improved gazelle optimization[J]. Systems Engineering and Electronics, 2026, 48(8): 2535-2544.

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评估指标	GWO	PSO	GOA	IGOA
最优值	1.500 7×10⁻⁴	1.496 7×10⁻⁴	1.495 6×10⁻⁴	1.493 4×10⁻⁴
平均值	1.684 3×10⁻⁴	2.116 9×10⁻⁴	1.513 8×10⁻⁴	1.501 0×10⁻⁴
标准差	1.822 0×10⁻⁵	3.014 7×10⁻⁴	2.094 8×10⁻⁶	3.851 6×10⁻⁷

算法	平均耗时
基于穷尽搜索的BP算法	435.017 8
GOA-BP	22.101 7
IGOA-BP	22.293 9

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基于改进瞪羚优化的被动合成孔径快速定位算法

Fast localization algorithm for passive synthetic aperture based on improved gazelle optimization

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