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

Abstract

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

CLC Number:

TN 973

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

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