Mao Yongjiang, Ren Wenjuan, Yang Zhanpeng
Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2021 Nov 10;21(22):7474. doi: 10.3390/s21227474.
With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time-frequency (T-F) analysis and a deep neural network to identify radar modulation signals. The T-F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T-F images. Adaptive filtering and morphological processing are used in T-F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T-F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is -10 dB, the probability of successful recognition (PSR) is 93.44%.
随着信号处理技术的发展以及新型雷达系统的使用，太空中出现了信号混叠和电子干扰。电磁信号在当前的太空应用中变得极其复杂，在低信噪比（SNR）环境下准确识别雷达调制信号方面引发了难题。为解决这一问题，本文提出一种将时频（T-F）分析与深度神经网络相结合的智能识别方法来识别雷达调制信号。采用复Morlet小波变换（CMWT）方法的时频分析来提取信号特征并获取时频图像。在时频图像增强中使用自适应滤波和形态学处理以减少噪声对信号特征的干扰。使用具有通道可分离ResNet（Sep-ResNet）的深度神经网络对增强后的时频图像进行分类。所提方法在低信噪比环境下完成了对雷达调制信号的高精度智能识别。当信噪比为-10 dB时，成功识别概率（PSR）为93.44%。
