基于蛇优化的单站机载无源定位算法

doi:10.12305/j.issn.1001-506X.2026.07.02

Abstract

Abstract:

Aiming at the problem that the positioning accuracy needs to be improved in the single station airborne positioning environment, a single station passive positioning method based on the meta-heuristic snake optimizer （SO） algorithm is proposed. This method uses a phase interferometer to measure the phase difference of the target, constructs a cost function, and optimizes the cost function to solve the target position through the SO algorithm. In order to verify the effectiveness of the proposed method, the airborne positioning models with three flight modes are simulated and the positioning test data at different frequencies are compared. Experimental results show that compared with the classical intelligent algorithms, the positioning accuracy of SO algorithm is improved by about 1.4% in the low phase difference error scenario. In scenarios with large phase difference errors, the positioning accuracy is improved by about 2%. In the analysis of the positioning performance of target radiation sources in different frequency bands, the positioning accuracy is improved by about 2.3%. SO algorithm can effectively improve the accuracy of single station airborne positioning, and has good adaptability and application value in complex electromagnetic environments.

Key words: target positioning, phase difference positioning, snake optimization （SO） algorithm, geometric dilution of precision

CLC Number:

TN 971

Limin GUO, Yifei ZHANG, Sida MA. Single station airborne passive positioning algorithm based on snake optimizer[J]. Systems Engineering and Electronics, 2026, 48(7): 2144-2152.

Figures/Tables 12

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References 30

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测试函数	坐标范围/km
$ {F_1}(x) = \displaystyle\sum\limits_{i = 1}^n {x_i^2} $	[−100, 100]
$ {F_2}(x) = \displaystyle\sum\limits_{i = 1}^n {\left\| {{x_i}} \right\|} + \displaystyle\prod\limits_{i = 1}^n {\left\| {{x_i}} \right\|} $	[−10, 10]
$ {F_3}(x) = \displaystyle\sum\limits_{i = 1}^n {{{\left( { \displaystyle\sum\limits_{j = 1}^i {{x_j}} } \right)}^2}} $	[−100, 100]
$ {F_4}(x) = \max \left\{ {\left\| {{x_i}} \right\|,1 \leqslant i \leqslant n} \right\} $	[−100, 100]
$ {F_5}(x) = \displaystyle\sum\limits_{i = 1}^{n - 1} {\left[ {100{{( {{x_{i + 1}} - x_i^2} )}^2} + {{\left( {{x_i} - 1} \right)}^2}} \right]} $	[−30, 30]
$ {F_6}(x) = \displaystyle\sum\limits_{i = 1}^n {{{\left( {\left[ {{x_i} + 0.5} \right]} \right)}^2}} $	[−100, 100]

频率/GHz	本文算法/%	GWO算法/%	PSO算法/%
2	97.7	96.1	85.1
6	91.4	90.3	71.5
10	89.6	88.5	63.3
14	80.9	83.4	58.6
16	78.5	70.2	56.8

频率/GHz	本文算法/%	GWO算法/%	PSO算法/%
2	93.8	90.3	80.2
6	91.2	85.4	65.5
10	88.4	80.5	61.2
14	79.3	75.8	52.5
16	75.1	69.7	48.3

频率/GHz	本文算法/%	GWO算法/%	PSO算法/%
2	95.5	93.2	85.3
6	92.5	88.4	69.6
10	89.3	85.8	61.5
14	80.7	79.2	55.2
16	75.6	74.9	50.6

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Single station airborne passive positioning algorithm based on snake optimizer

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