基于MSST及HOG特征提取的雷达辐射源信号识别

针对传统雷达信号识别算法在低信噪比下识别准确率低的问题，提出了基于多重同步压缩（MSST）时频变换及方向梯度直方图（HOG）特征提取的雷达辐射源信号识别算法。所提算法在雷达时域信号短时傅里叶变换（STFT）基础上进行多重同步压缩处理获得信号时频分布图，通过HOG算子对信号时频分布图进行HOG特征提取，将提取的HOG特征通过主成分分析法（PCA）进行降维，将降维后的特征参数送入支持向量机（SVM）对雷达信号进行分类与识别。实验结果表明：所提算法具有较低的复杂度，当信噪比为-8 dB时，仿真实验与半实物仿真实验针对9种典型雷达信号的识别准确率达到90%以上。

Radar emitter signal recognition based on MSST and HOG feature extraction

Aiming at the problem of low recognition accuracy of traditional radar signal recognition algorithms under low signal-to-noise ratio, a radar emitter recognition algorithm based on multi-synchrosqueezing transform (MSST) time-frequency transformation and histogram of direction gradient (HOG) feature extraction is proposed. The algorithm performs multiple synchronous compression processing on the basis of the short-time Fourier transform (STFT) of the radar time domain signal to obtain the signal time-frequency distribution image, then uses the HOG operator to extract the HOG feature of the signal time-frequency distribution image. The HOG features are dimensionally reduced by principal component analysis (PCA), and finally the feature parameters after dimension reduction are fed into the support vector machine (SVM) to classify and identify the radar signal. The experimental results show that the algorithm has low complexity, and when the signal-to-noise ratio is ?8 dB, the recognition accuracy of the simulation experiments and hardware-in-the-loop simulation experiments for 9 typical radar signals can reach more than 90%.