基于频响特性的大起伏密集假目标干扰识别技术

doi:10.12382/bgxb.2022.0610

摘要/Abstract

摘要：

转发式密集假目标干扰具有压制性和欺骗性干扰的特点,干扰信号与目标回波极为相似,识别难度大。基于密集假目标干扰产生机理和射频链路物理特性,系统研究并提出雷达和干扰机频率响应特性及其对真、假目标回波幅-频映射特征的影响机制模型;针对干扰信号功率动态范围较大的实际情况,提出大动态信/干噪比条件下基于频响特性的密集假目标干扰识别方法;通过卷积神经网络-长短时记忆网络双通道特征融合的基分类器构建和M/N逻辑分类器集成,实现真-假回波信号频响起伏特征提取与分类识别。研究结果表明:所提方法在实测数据上获得了94.5%以上的识别精度,证明了其有效性和先进性;所做工作对大起伏密集假目标干扰识别具有参考意义。

关键词: 雷达干扰识别, 大起伏密集假目标, 幅频响应, 双通道集成神经网络

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

中图分类号:

TN974

韦文斌, 彭锐晖, 孙殿星, 张家林, 王向伟. 基于频响特性的大起伏密集假目标干扰识别技术[J]. 兵工学报, 2023, 44(10): 3204-3217.

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.

图/表 14

图1 目标回波频响特性物理模型

Fig.1 Physical model of target echo frequency response characteristics

图2 干扰信号频响特性物理模型

Fig.2 Physical model of jamming signal frequency response characteristics

图3 目标-干扰信号识别算法流程

Fig.3 Target-jamming signal recognition algorithm

表1 仿真参数设置

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

图4 幅频响应曲线仿真模型

Fig.4 Simulation model of amplitude frequency response curves

图5 干信比为0dB仿真识别结果

Fig.5 Simulation recognition results when JSR is 0dB

图6 干信比为3dB仿真识别结果

Fig.6 Simulation recognition results with JSR=3dB

图7 干信比为5dB仿真识别结果

Fig.7 Simulation recognition results with JSR=5dB

图8 信噪比为-10~10dB动态范围情况仿真识别结果

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

图9 不同信噪比动态范围中心值下5种算法性能对比

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

图10 实测样本幅频响应

Fig.10 Amplitude frequency response of the measured sample

图11 本文方法与文献[16]方法对比结果

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

图12 本文方法与文献[17]方法对比结果

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

表2 大起伏条件下识别结果对比

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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