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基于多幅图像特征的 LPI 雷达波形识别。

LPI Radar Waveform Recognition Based on Features from Multiple Images.

机构信息

College of Physical Science and Technology, Central China Normal University, No.152 Luoyu Road, Wuhan 430079, China.

Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.

出版信息

Sensors (Basel). 2020 Jan 17;20(2):526. doi: 10.3390/s20020526.

DOI:10.3390/s20020526
PMID:31963588
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7014522/
Abstract

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is -6 dB.

摘要

实时检测和分类截获的噪声式低截获概率(LPI)雷达信号的调制类型是电子情报系统的必要生存技术。大多数雷达信号都被设计为具有 LPI 特性;因此,最近越来越多的人关注 LPI 雷达波形识别技术(LWRT)。在本文中,我们提出了一种具有两种不同特征提取结构的多特征图像联合决策(MFIJD)模型,该模型充分提取了像素特征,以获得每个特征图像的预分类结果,从而应对大多数 LPI 雷达信号的非平稳特性。该模型的核心技术是将短时自相关特征图像、双短时自相关特征图像和原始信号时频图像(TFI)同时输入到混合模型分类器中,该分类器适用于非平稳信号,具有更高的通用性。我们通过模拟本文定义的 11 种信号并生成训练集和测试集来演示 MFIJD 的性能。与文献的比较表明,所提出的方法不仅对 LPI 雷达信号具有很高的通用性,而且在低信噪比(SNR)环境下更适应 LPI 雷达波形识别。当 SNR 为-6dB 时,该方法的整体识别率达到 87.7%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/6ecddad8bd3d/sensors-20-00526-g022.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/c918b874ece5/sensors-20-00526-g017.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/6447c830c32e/sensors-20-00526-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/2e639b84aed1/sensors-20-00526-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/6d4dfbcc7e8e/sensors-20-00526-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/6223a466c007/sensors-20-00526-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/d733183838be/sensors-20-00526-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/57c1dd28680a/sensors-20-00526-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/c50879254a48/sensors-20-00526-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/424d4dd5d555/sensors-20-00526-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/c918b874ece5/sensors-20-00526-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/0ffee235141d/sensors-20-00526-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbab/7014522/c4ec4137d833/sensors-20-00526-g019.jpg
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本文引用的文献

1
LPI Radar Waveform Recognition Based on Time-Frequency Distribution.基于时频分布的线性调频中断连续波雷达波形识别
Sensors (Basel). 2016 Oct 12;16(10):1682. doi: 10.3390/s16101682.
线性和非线性调频雷达信号瞬时频率估计的比较研究
Sensors (Basel). 2021 Apr 17;21(8):2840. doi: 10.3390/s21082840.
4
Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning.基于自适应奇异值重构和深度残差学习的雷达信号调制识别
Sensors (Basel). 2021 Jan 10;21(2):449. doi: 10.3390/s21020449.