Recognition of Dense False Target Jamming with Large Fluctuations Using Frequency Response Characteristics

doi:10.12382/bgxb.2022.0610

Abstract

Abstract:

Forwarding dense false target jamming is suppressive and deceptive jamming, with jamming signals quite similar to those of the target echo, thus posing challenges for recognition. Based on the generation mechanism of dense false target jamming and the physical characteristics of the Ratio Frequency link, the frequency response characteristics of radar and jammer and the mechanism model of their influence on the amplitude-frequency mapping characteristics of true and false target echoes are systematically studied and proposed. On this basis, leveraging the large dynamic range of jamming signal power, a method of dense false target jamming recognition based on frequency response characteristics and large dynamic SNR/JNR is proposed. Through the construction of basis classifier with a dual-channel feature fusion network comprising convolutional neural network and long short-memory network (ODCNN-LSTM), the M/N logical criteria is used to integrate the basis classifiers. Then, feature extraction and recognition of true and false echo signal frequency response fluctuations are realized. The recognition accuracy is over 94.5% for the measured data, demonstrating the effectiveness and innovation of the proposed method. This work holds significance for recognizing dense false target jamming with significant fluctuations.

Key words: radar jamming recognition, dense false target jamming, amplitude frequency, dual-channel integrated neural network

CLC Number:

TN974

WEI Wenbin, PENG Ruihui, SUN Dianxing, ZHANG Jialin, WANG Xiangwei. Recognition of Dense False Target Jamming with Large Fluctuations Using Frequency Response Characteristics[J]. Acta Armamentarii, 2023, 44(10): 3204-3217.

Figures/Tables 14

Fig.1 Physical model of target echo frequency response characteristics

Fig.2 Physical model of jamming signal frequency response characteristics

Fig.3 Target-jamming signal recognition algorithm

Table 1 Parameter setting for simulation

参数	取值	参数	取值
脉冲宽度/μs	10	目标距离/km	40
载频/GHz	3	训练样本数	2 000
带宽/MHz	20	测试样本数	400
采样频率/MHz	40	干扰距离/km	40~60
目标多普勒频率/kHz	8	干扰多普勒频率/kHz	-8~8

Fig.4 Simulation model of amplitude frequency response curves

Fig.5 Simulation recognition results when JSR is 0dB

Fig.6 Simulation recognition results with JSR=3dB

Fig.7 Simulation recognition results with JSR=5dB

Fig.8 Simulation and recognition results of dynamic range with SNR=-10-10dB

Fig.9 Performance comparison of five algorithms with SNR dynamic ranges of different center values

Fig.10 Amplitude frequency response of the measured sample

Fig.11 Comparison between our method and the method in Ref.[16]

Fig.12 Comparison between our method and the method in Ref.[17]

Table 2 Comparison of recognition results under large fluctuation conditions%

信噪比中心值/dB	本文方法	文献[27]方法
0	79.7	64.6
2	84.5	65.5
4	89.5	65.6
6	92.3	66.0
8	95.3	66.3
10	97.5	67.9

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