外辐射源雷达互模糊函数智能快速计算方法

doi:10.19665/j.issn1001-2400.20231003

Abstract

Abstract:

The passive radar system realizes the target detection by receiving the direct wave signal from the emitter and the target echo signal.The cross ambiguity function is an important means to improve the coherent accumulation of the echo signal.However,the echo signal received by the passive radar is very weak,so it is necessary to increase the accumulation time to improve the estimation accuracy.When the target speed is fast,the frequency search range increases.In order to achieve a range of target detection requirements and take into account the real-time performance of data processing,it is of great significance to study the fast calculation method of the cross ambiguity function,and due to the objective requirements of long-time accumulation and large-scale time-frequency search,the computation of the cross ambiguity function is huge,which makes it difficult for the traditional accelerated calculation method based on ergodic search to meet the real-time requirements of system processing.In order to improve the efficiency of cross ambiguity function optimization,a time-frequency difference calculation method based on multi-group feature optimization is proposed in this paper.By deeply analyzing the characteristics of typical digital TV signals,a two-stage cross ambiguity intelligent optimization fast calculation method based on target characteristics is designed in the framework of particle swarm optimization theory.By designing an effective search strategy,this method introduces the multi-population iteration mechanism and shrinkage factor,which avoids the disadvantages of the traditional method of redundant computation.On the premise of ensuring the calculation accuracy,the time-frequency point calculation is greatly reduced,and the search efficiency of cross ambiguity function is improved.

Key words: passive radar, ambiguity function, particle swarm optimization, target detection

CLC Number:

TN959.16

CHE Jibin, WANG Changlong, JIA Yan, REN Zizheng, LIU Chunheng, ZHOU Feng. Fast algorithm for intelligent optimization of the cross ambiguity function of passive radar[J].Journal of Xidian University, 2023, 50(6): 21-33.

Figures/Tables 17

算法类型	复乘计算量
直接遍历计算	N×2K×2N
文献[14]	$1 + 32 l b N$ N×2K
文献[15]	$1 + 32 l b N$ ×N× $2 K F s t e p + 2 F s t e p 1 / (2 b) × m$
文中算法	Gen× $1 + 32 l b N$ ×N×Q×γ

References 21

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参数	取值
信号载频/GHz	12.5
接收机带宽/MHz	10
辐射源坐标/km	(0,0,35 860)
接收机坐标/km	(1,0,0)
目标初始位置/km	(0,15,20)
目标RCS/m²	10
接收机温度/K	308.15
发射功率/kW	10
发射增益/dB	40
接收增益/dB	40

参数	取值
小种群个数H	7
小种群规模G_i	5
小种群迭代次数X₁	5
种群最大迭代次数X	20
学习因子c₁	2.5
学习因子c₂	1.5
粒子最大速度V值	100
权重系数ω	0.8
合并后有连续更新的代数M	5

Fast algorithm for intelligent optimization of the cross ambiguity function of passive radar

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References 21

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