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摘要:
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。
Abstract: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%.
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图 8 九种雷达信号识别准确率混淆矩阵
Figure 8. Confusion matrix of recognition rate of 9 kinds of radar signals
图 9 四种算法平均识别准确率对比
Figure 9. Comparison of average recognition rates of four algorithms
图 10 雷达辐射源信号识别半实物仿真实验平台
Figure 10. Hardware-in-the-loop simulation experiment platform for radar radiation source signal recognition
图 11 基于半实物仿真实测数据的识别准确率
Figure 11. Recognition accuracy based on hardware-in-the-loop simulation data
表 1 梯度方向和梯度值
Table 1. Gradient direction and gradient values
梯度方向/(°) 90 150 80 70 10 105 140 30 梯度值 30 68 4 38 36 12 17 8 
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表 2 算法训练时间
Table 2. Algorithm training time
算法 训练
样本训练时间/s 信噪比
−8 dB信噪比
−6 dB信噪比
−4 dB信噪比
−2 dB信噪比
0 dBAlexNet 3600 867 865 862 861 853 SVM 3600 67 66 61 57 54
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表 4 训练集和测试集在不同信噪比下的整体识别准确率
Table 4. Overall recognition rate of training set and test set under different SNR
训练集信噪比/dB 测试集信噪比/dB 总体识别准确率/% −14 −6 93.06 2 10 100 −2 −10 90.22 10 2 97.61 −14~10 −14~10 92.02
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