基于差分进化算法的多基地声纳目标定位

doi:10.12305/j.issn.1001-506X.2026.06.04

Abstract

Abstract:

To address the insufficient localization accuracy and stability of traditional closed-form solutions and part optimization algorithms in multi-static sonar target localization with complex noise conditions, the differential evolution （DE） algorithm is employed in this study to solve the localization model. By analyzing the geometric relationships of signal transmission paths, a hyperbolic localization model based on time difference of arrival is established, and the core idea and mathematical procedures of solving the localization model using the DE algorithm are elaborated in detail. Simulation experiments are conducted to determine the key DE algorithm parameters suitable for various noise levels. The results validate that with conditions involving sensor-position, sound-speed, and time-delay measurement errors, the DE algorithm achieves superior localization performance compared to both the simulated annealing algorithm and the Newton iteration method. Moreover, the spatial distribution characteristics of localization errors are investigated. Although the localization accuracy gradually decreases with increasing distance from the geometric center of the sensor network, the algorithm remains effective in achieving target localization. The research demonstrates the feasibility and robustness of applying the DE algorithm for target localization in multi-static sonar systems.

Key words: differential evolution algorithm, multi-static sonar, time difference of arrival, target localization

CLC Number:

TB 566

Qiangqiang WEI, Linzhou XU, Dong HAN, Yutong GUO. Target localization for multi-static sonar based on differential evolution algorithm[J]. Systems Engineering and Electronics, 2026, 48(6): 1819-1829.

Figures/Tables 17

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References 28

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噪声水平	测量误差类别
噪声水平	${\sigma _p}$/m	${\sigma _c}$/（m/s）	${\sigma _t}$/ms
低噪声	10	5	10
中噪声	50	25	50
高噪声	100	50	100

最大迭代次数	种群数量$N$	缩放因子$F$	交叉概率${\mathrm{CR}}$
100	5	0.1 ~ 2（步进0.1）	0.05 ~ 0.95（步进0.05）
	10	0.1 ~ 2（步进0.1）	0.05 ~ 0.95（步进0.05）
	20	0.1 ~ 2（步进0.1）	0.05 ~ 0.95（步进0.05）
	50	0.1 ~ 2（步进0.1）	0.05 ~ 0.95（步进0.05）
	100	0.1 ~ 2（步进0.1）	0.05 ~ 0.95（步进0.05）

噪声水平	DE算法	SA算法	牛顿迭代算法
低噪声	13.43	14.85	9.05
中噪声	13.50	14.95	9.39
高噪声	13.64	15.14	9.26

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Target localization for multi-static sonar based on differential evolution algorithm

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