基于聚类和时序相关的重点雷达信号快速识别

doi:10.3969/j.issn.1001-506X.2020.03.013

摘要/Abstract

摘要：

针对传统雷达信号识别方法对重点目标识别的针对性、时效性不强的问题,提出一种基于聚类和时序相关的重点雷达信号实时识别方法。首先,依据具有噪声的基于密度的聚类(density-based spatial clustering of application with noise, DBSCAN)算法对侦获信号的脉冲描述字进行分选;而后,利用分选所得脉冲的时序特征与重点目标信号脉冲重复间隔(pulse repetition interval, PRI)生成仿真信号;最后,计算仿真信号的互相关函数,基于相关度判断PRI参数是否匹配。仿真实验表明:所提方法明显提升了对重点目标信号的识别时效,能够应对存在噪声干扰和信号交叠的复杂信号环境,对局部脉冲参数丢失不敏感。

关键词: 雷达信号识别, 基于密度的具有噪声的聚类算法, 脉冲描述字, 时序相关

Abstract:

Aiming at the pertinence and ineffectiveness of the traditional radar signal recognition method in identifying key targets, a real-time radar signal recognition method based on clustering and time-series correlation is proposed. Firstly, pulse description words of detected signals are sorted based on density-based spatial clustering of the application with noise (DBSCAN) algorithm. Then, the timing characteristics of the sorting pulse and the pulse repetition interval (PRI) parameters of the key target signal are used to generate the simulation signal. Finally, the cross-correlation function of the simulated signal is calculated, and the PRI parameter is judged to be matched based on the degree of correlation. Simulation results show that the proposed method significantly improves the identification time of key target signals, can deal with the complex signal environment with noise interference and overlapping signals, and is not sensitive to the loss of local pulse parameters.

Key words: radar signal recognition, density-based spatial clustering of application with noise (DBSCAN) algorithm, pulse description words, time-series correlation

中图分类号:

张怡霄, 郭文普, 康凯, 姚云龙, 王攀. 基于聚类和时序相关的重点雷达信号快速识别[J]. 系统工程与电子技术, 2020, 42(3): 597-602.

Yixiao ZHANG, Wenpu GUO, Kai KANG, Yunlong YAO, Pan WANG. Key radar signal fast recognition method based on clustering and time-series correlation[J]. Systems Engineering and Electronics, 2020, 42(3): 597-602.

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序号	1	2	3	4	5	6	干扰
DOA/(°)	36~39	25~30	40~43	43~44	42~45	63~65
RF/MHz	6 450~6 650捷变	6 600固定	6 200~6 500捷变	6 400、6 500、6 450、6 300跳变	6 600~6 800捷变	6 600固定
PW/μs	5.10~11.40	9.65	14.5	15.0~20.0	10.5~15.5	9.65
PRI/μs	800×5/830×5/900×5/780×5/820×5/930×5脉组参差	400//440抖动	670固定	1 500固定	1 200//1 250滑变,增量为10	400//440抖动
脉冲数	600	400	500	500	200	300	500

序号	雷达1	雷达2	雷达3	雷达4	雷达5
RF/MHz	6 450~6 650捷变	6 600固定	6 200~6 500捷变	6 600~6 800捷变	5 600固定
PW/μs	5.10~11.40	9.65	14.5	10.5~15.5	12
PRI/μs	800×5/830×5/900×5/780×5/820×5/930×5脉组参差	400//440抖动	670固定	1 200//1 250滑变,增量为10	1 850//1 900抖动

序号	F₀, P_W0	ΔFΔ, P_W	PRI序列	仿真脉冲数
雷达1	6 550, 8.25	300, 8	800×5, 830×5, 900×5, 780×5, 820×5, 930×5	30
雷达2	6 600, 9.65	100, 2	400, 410, 420, 430, 440	40
雷达3	6 350, 14.5	250, 2	670	40
雷达4	6 700, 13	200, 4	1 200, 1 210, 1 230, 1 240, 1 250	30
雷达5	5 600, 12	100, 2	1 875	50

序号	雷达1	雷达2		雷达3	雷达4	雷达5
聚类数	611	401	301	503	206	0
正确聚类数	600	400	300	500	200	-
聚类后信噪比/dB	17.36	26.02	24.77	22.22	15.23	-

序号	对应序列时间	识别时间(相关度稳定时间)
雷达1	0~25 300	25 300
雷达2	100~16 900	16 900
雷达2	500~17 300	17 300
雷达3	200~27 000	27 000
雷达4	300~45 300	49 400