基于多尺度信息熵的雷達(dá)輻射源信號識別
doi: 10.11999/JEIT180535
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西南交通大學(xué)電氣工程學(xué)院? ?成都? ?610031
Radar Emitter Signal Identification Based on Multi-scale Information Entropy
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College of Electrical Engineering, Southwest Jiao Tong University, Chengdu 610031, China
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摘要:
隨著雷達(dá)信號的日益復(fù)雜，從實數(shù)序列中提取特征變得越來越困難，但當(dāng)它們表示成符號序列時，通常能更容易地挖掘出有效的特征參數(shù)。因此，該文提出一種基于多尺度信息熵(MSIE)的雷達(dá)信號識別方法。首先通過符號聚合近似(SAX)算法在不同字符集尺度下將雷達(dá)信號轉(zhuǎn)換為符號化序列；然后聯(lián)合各符號序列的信息熵值，組成MSIE特征向量；最后，使用k鄰近算法(k-NN)作為分類器實現(xiàn)雷達(dá)信號的分類識別。通過仿真6種典型的雷達(dá)信號進(jìn)行驗證，結(jié)果表明該方法在信噪比(SNR)為5 dB時，不同雷達(dá)信號的識別正確率大于90%，并且優(yōu)于傳統(tǒng)的基于復(fù)雜度特征(盒維數(shù)和稀疏性)的識別方法。
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關(guān)鍵詞:
- 雷達(dá)信號識別 /
- 符號聚合近似算法 /
- 多尺度信息熵 /
- k鄰近算法
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 參數(shù)a從3～8的等概率斷點查詢表[12]
斷點(${\beta _i}$) 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種雷達(dá)信號的識別率
雷達(dá)信號 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種方法的總體識別正確率比較(%)
識別方法 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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