基于时频特征提取和残差神经网络的雷达信号识别

针对低信噪比(signal to noise ratio,SNR)下雷达信号脉内调制类型识别率较低的问题,提出了基于时频特征提取和残差神经网络的雷达信号识别算法.时频特征提取首先通过分数阶傅里叶变换对信号进行Chirp基分解,按照Chirp基载频与调频率的不同组合对信号划分类别,并设置对应的分类特征参数.然后,计算信号...

Radar signal recognition based on time-frequency feature extraction and residual neural network

Aiming at the problem of low recognition rate of radar signal pulse modulation type under low signal to noise ratio(SNR),a radar signal recognition algorithm based on time-frequency feature extraction and residual neural network is proposed.The time-frequency feature extraction firstly performs chirp-based decomposition of the signal through the fractional Fourier transform,classifies the signal according to different combinations of chirp-based carrier frequency and frequency modulation,and sets the corresponding classification feature parameters.Then,the pseudo Wigner-Ville time-frequency distribution of the signal is calculated and Zernike moments is extracted.The above-mentioned characteristic parameters form a signal characteristic vector,and a residual neural network classifier is used to realize radar signal recognition.Simulation results show that the recognition accuracy can reach more than 93%when SNR is under-2 dB.At the same time,the robustness is well verified,and the algorithm complexity can meet the actual requirements.