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

摘要/Abstract

摘要：

互模糊运算是外辐射源雷达系统中回波信号与参考信号相干积累的重要手段,然而,外辐射源雷达接收到的目标回波信号非常微弱,需要增加积累时间来提高估计精度;当目标速度较快时,频率搜索范围增加。为了实现一定范围的目标检测需求并兼顾数据处理的实时性,研究互模糊函数快速计算方法具有重要的意义。由于长时间积累与大范围空域搜索的客观需求,导致互模糊函数运算量极大,需要消耗大量计算资源。针对以上问题,通过分析典型静止轨道数字电视信号模糊函数的特点,以粒子群优化理论为框架,设计了一种多种群特征寻优时频差估计算法。该方法引入多种群迭代机制和收缩因子,通过设计有效的搜索策略和粒子更新方法,避免了传统方法冗余计算量大的问题。在保证计算精度的前提下,该方法大幅度减少了模糊函数的计算量,提高了互模糊函数计算搜索效率,大幅度减少了所需的计算量。

关键词: 外辐射源雷达, 互模糊函数, 粒子群优化, 目标检测

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

中图分类号:

TN959.16

车吉斌, 王常龙, 贾岩, 任紫正, 刘春恒, 周峰. 外辐射源雷达互模糊函数智能快速计算方法[J]. 西安电子科技大学学报, 2023, 50(6): 21-33.

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.

图/表 17

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表1

几种模糊函数算法复乘计算量"

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

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表2

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表3

图9

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图11

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图14

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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