基于棧式稀疏自編碼器的低信噪比下低截獲概率雷達信號調(diào)制類型識別
doi: 10.11999/JEIT170588 cstr: 32379.14.JEIT170588
基金項目:
國家自然科學基金(61571146)，中央高?；究蒲袠I(yè)務(wù)費專項資金(HEUCFP201769)
Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder
Funds:
The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769)
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摘要: 針對低截獲概率(LPI)雷達信號識別率低且特征提取困難的問題，該文提出一種基于Choi-Williams分布(CWD)和棧式稀疏自編碼器(sSAE)的自動分類識別系統(tǒng)。該系統(tǒng)從反映信號本質(zhì)特征的時頻圖像入手，首先對LPI雷達信號進行CWD時頻分析，獲取2維時頻圖像；然后對得到的時頻原始圖像進行預(yù)處理，并把預(yù)處理后的圖像送入多層稀疏自編碼器(SAE)進行離線訓練；最后把SAE自動提取的特征輸入softmax分類器，實現(xiàn)雷達信號的在線分類識別。仿真結(jié)果表明，信噪比為時，該系統(tǒng)對8種LPI雷達信號(LFM, BPSK, Costas, Frank和T1~T4)的整體平均識別率達到96.4%，在低信噪比條件下明顯優(yōu)于人工設(shè)計提取信號特征的識別方法。Abstract: In order to solve the problem that the correct recognition rate of Low Probability of Intercept (LPI) radar signal is low and the feature extraction is difficult, an automatic classification and recognition system based on Choi-Williams Distribution (CWD) and stacked Sparse Auto-Encoder (sSAE) is proposed. The system starts from the time-frequency image which reflects the essential characteristics of the signal. Firstly, the CWD is performed on the LPI radar signal to obtain the two-dimensional time-frequency image. Then, the obtained time-frequency original image is preprocessed and the preprocessed image is sent into the multilayer SAE for off-line training. Finally, the feature automatically extracted from the SAE is sent to the softmax classifier, to achieve on-line classification and identification of the radar signal. Simulation results show that the classification system achieves overall correct recognition rate of 96.4% at SNR of for the eight LPI radar signals (LFM, BPSK, Costas, Frank and T1~T4), which is better than the method of manually designing the extract signal characteristics under low SNR conditions.
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