Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1904-1911.doi: 10.13229/j.cnki.jdxbgxb20210173

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

Qiang GUO(),Ming-song LI,Kai ZHOU   

  1. College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China
  • Received:2021-03-05 Online:2022-08-01 Published:2022-08-12

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

Table 1

Parameter setting of radar signal sample"

雷达序号工作状态DOA /(°)PW/nsRF/MHz脉冲数量
1169~72450多脉宽9200~9300频率跳变100
269~72350捷变9300~9400多载频95
369~72450多脉宽9400~9500多载频90
2161~64550捷变8400~8800频率捷变100
3163~66250捷变8900频率固定95
263~66350多脉宽8800~8900频率捷变85
363~66450捷变8900~9000频率捷变90
4165~68150捷变8900~9300多载频100

Fig.1

Distribution of radar signal sample"

Fig.2

Curve of potential entropy change"

Fig.3

Result graph of cluster"

Table 2

Membership statistics"

12345678
10.710.580.040.140.120.130.40
20.800.030.100.080.090.28
30.020.080.070.080.23
40.250.310.270.11
50.810.860.40
60.830.33
70.38
8

Fig.4

Classification result of algorithm in this paper"

Table 3

Classification accuracy in this paper"

雷达标号1234
脉冲样本数量285100270100
正确分选2859827099
错误分选0201
干扰点错误分选0
正确率/%99.6
仿真运行时间/s1.991 377

Table 4

Contrast of sorting effect"

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

Table 5

Classification accuracy in this paper"

丢失率/%准确率统计/%
1099.87
2099.87
3099.74
4099.60
5099.87
6099.87

Table 6

Parameter setting of radar signal sample"

雷达

序号

工作

状态

DOA /(°)PW/nsRF/MHz

脉冲

数量

1139~42450多脉宽4200~4300频率跳变100
241~44350捷变4300~4400多载频100
339~41530多脉宽4400~4500多载频100
2136~41185捷变3900~4300频率捷变100
3133~36250捷变3895~3995频率跳变100
234~37345多脉宽3800~3900频率捷变100
332~35450捷变3900~4000频率捷变100
4134~38435捷变3450~3900多载频100

Fig.5

Distribution of radar signal sample and classification results"

Table 7

Contrast of sorting effect"

算 法正确分选率/%
基于数据场的多模雷达信号分选62.1
数据场联合PRI变换与聚类的雷达信号分选46.8
基于空间数据挖掘的多模雷达信号分选94.1
本文算法94.4
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