Radar Emitter Signal Identification Based on Multi-scale Information Entropy
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摘要:
随着雷达信号的日益复杂，从实数序列中提取特征变得越来越困难，但当它们表示成符号序列时，通常能更容易地挖掘出有效的特征参数。因此，该文提出一种基于多尺度信息熵(MSIE)的雷达信号识别方法。首先通过符号聚合近似(SAX)算法在不同字符集尺度下将雷达信号转换为符号化序列；然后联合各符号序列的信息熵值，组成MSIE特征向量；最后，使用k邻近算法(k-NN)作为分类器实现雷达信号的分类识别。通过仿真6种典型的雷达信号进行验证，结果表明该方法在信噪比(SNR)为5 dB时，不同雷达信号的识别正确率大于90%，并且优于传统的基于复杂度特征(盒维数和稀疏性)的识别方法。
Abstract:With the increasing complexity of radar signals, it is more and more difficult to extract features of the real sequences, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters. Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed. Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales. Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector. Finally, the k-Nearest Neighbor (k-NN) is used as a classifier to realize the classification and identification of radar signals. The simulation results of 6 typical radar signals show that using the proposed method the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and better performance can be obtaned conpared with the traditional identification method based on complexity characteristics (box-dimension and sparseness).
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表 1 参数a从3～8的等概率断点查询表[12]
断点(${\beta _i}$) 字符集大小(a) 3 4 5 6 7 8 ${\beta _{{1}}}$ 0.43 0.67 0.84 0.97 1.07 1.15 ${\beta _{{2}}}$ 0.43 0 0.25 0.43 0.57 0.67 ${\beta _{{3}}}$ – 0.67 0.25 0 0.18 0.32 ${\beta _{{4}}}$ – – 0.84 0.43 0.18 0 ${\beta _{{5}}}$ – – – 0.97 0.57 0.32 ${\beta _{{6}}}$ – – – – 1.07 0.67 ${\beta _{{7}}}$ – – – – – 1.15 
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表 2 不同SNR下6种雷达信号的识别率
雷达信号 信噪比SNR (dB) 20 15 10 5 LFM 1.000 1.000 1.000 0.985 CP 1.000 1.000 1.000 1.000 BPSK 0.975 0.990 0.990 1.000 BFSK 0.930 0.910 0.800 0.700 NLFM 1.000 1.000 1.000 1.000 COSTAS 1.000 1.000 1.000 1.000
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表 5 3种方法的总体识别正确率比较(%)
识别方法 信噪比SNR(dB) 20 15 10 5 MSIE+k-NN 98.42 97.25 94.25 91.25 CC+k-NN 80.25 73.08 54.33 <50 SIE+k-NN 81.42 79.08 71.25 62.92
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