基于势距图与改进云模型的多模雷达分选

doi:10.13229/j.cnki.jdxbgxb20210173

Abstract

Abstract:

In order to solve the problems of poor accuracy of multi-mode radar signal sorting，a new multi-mode radar signal sorting algorithm based on potential distance graph combined with PCA and improved cloud model through the research of machine learning algorithm is proposed. Firstly， after eliminating the interference points，the potential distance graph is used to cluster. Signal samples can be assigned to each cluster center point after only one traversal without iteration. Then principal component analysis （PCA） is used to reduce dimension and extract key factors of each feature to form new features. Finally， the improved cloud model is used to analyze the new features. The average membership degree between classes is obtained. The evaluation criteria are established to complete the final classification of radar signals. Compared with the existing algorithms， simulation results show that this method has higher accuracy and strong anti-interference ability. To a certain extent， it can solve the problem of wrong sorting of multi-mode radar signals.

Key words: signal sorting, potential distance graph, principal component analysis, improved cloud model, multi-mode radar

CLC Number:

TN957.5

Qiang GUO,Ming-song LI,Kai ZHOU. Multi⁃mode radar signal sorting based on potential distance graph and improved cloud model[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1904-1911.

Figures/Tables 12

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雷达序号	工作状态	DOA /（°）	PW/ns	RF/MHz	脉冲数量
1	1	69~72	450多脉宽	9200~9300频率跳变	100
	2	69~72	350捷变	9300~9400多载频	95
	3	69~72	450多脉宽	9400~9500多载频	90
2	1	61~64	550捷变	8400~8800频率捷变	100
3	1	63~66	250捷变	8900频率固定	95
	2	63~66	350多脉宽	8800~8900频率捷变	85
	3	63~66	450捷变	8900~9000频率捷变	90
4	1	65~68	150捷变	8900~9300多载频	100

	2	3	4	5	6	7	8
1	0.71	0.58	0.04	0.14	0.12	0.13	0.40
2		0.80	0.03	0.10	0.08	0.09	0.28
3			0.02	0.08	0.07	0.08	0.23
4				0.25	0.31	0.27	0.11
5					0.81	0.86	0.40
6						0.83	0.33
7							0.38
8

雷达标号	1	2	3	4
脉冲样本数量	285	100	270	100
正确分选	285	98	270	99
错误分选	0	2	0	1
干扰点错误分选	0
正确率/%	99.6
仿真运行时间/s	1.991 377

算法	正确分选率/%
基于数据场的多模雷达信号分选	95.9
数据场联合PRI变换与聚类的雷达信号分选	52.3
基于空间数据挖掘的多模雷达信号分选	95.5
本文算法	99.6

丢失率/%	准确率统计/%
10	99.87
20	99.87
30	99.74
40	99.60
50	99.87
60	99.87

Multi⁃mode radar signal sorting based on potential distance graph and improved cloud model

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