基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别

郭立民; 寇韵涵; 陈涛; 张明

doi:10.11999/JEIT170588

基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别

doi: 10.11999/JEIT170588

基金项目:

国家自然科学基金(61571146)，中央高校基本科研业务费专项资金(HEUCFP201769)

计量
- 文章访问数: 1699
- HTML全文浏览量: 298
- PDF下载量: 236
- 被引次数: 0
出版历程
- 收稿日期: 2017-06-19
- 修回日期: 2017-11-21
- 刊出日期: 2018-04-19

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)

摘要

摘要: 针对低截获概率(LPI)雷达信号识别率低且特征提取困难的问题，该文提出一种基于Choi-Williams分布(CWD)和栈式稀疏自编码器(sSAE)的自动分类识别系统。该系统从反映信号本质特征的时频图像入手，首先对LPI雷达信号进行CWD时频分析，获取2维时频图像；然后对得到的时频原始图像进行预处理，并把预处理后的图像送入多层稀疏自编码器(SAE)进行离线训练；最后把SAE自动提取的特征输入softmax分类器，实现雷达信号的在线分类识别。仿真结果表明，信噪比为时，该系统对8种LPI雷达信号(LFM, BPSK, Costas, Frank和T1~T4)的整体平均识别率达到96.4%，在低信噪比条件下明显优于人工设计提取信号特征的识别方法。
- 低截获概率雷达 /
- CWD时频分析 /
- 图像预处理 /
- 深度学习 /
- 栈式稀疏自编码器
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.

HTML全文

参考文献(14)

SCHLEHER D C. LPI radar: Fact or fiction[J]. IEEE Aerospace and Electronic Systems Magazine, 2006, 21(5): 3-6. doi: 10.1109/MAES.2006.1635166.

PHILLIP E P. Detecting and Classifying Low Probability of Intercept Radar (Second Edition)[M]. Norwood, MA, USA, Artech House, 2009: 1-15.

王星, 周一鹏, 周东青, 等. 基于深度置信网络和双谱对角切片的低截获概率雷达信号识别[J]. 电子与信息学报, 2016, 38(11): 2972-2976. doi: 10.11999/JEIT160031.

WANG Xing, ZHOU Yipeng, ZHOU Dongqing, et al. Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice[J]. Journal of Electronics Information Technology, 2016, 38(11): 2972-2976. doi: 10.11999/JEIT160031.

LUNDEN J and KOIVUNEN V. Automatic radar waveform recognition[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(1): 124-136. doi: 10.1109/JSTSP.2007. 897055.

ZHANG Ming, LIU Lutao, DIAO Ming, et al. LPI radar waveform recognition based on time-frequency distribution[J]. Sensors, 2016, 16(10): 1682-1706. doi: 10.3390/s16101682.

HINTON G, OSINDERO S, TEH Y W, et al. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527.

BENGIO Y, LAMBIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks[C]. Advances in Neural Information Processing Systems, Hyatt Regency Vancouver, 2007: 153-160.

LECUN Y, BENGIO Y, HINTON G, et al. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539.

NG A. Sparse autoencoder[J]. CS294A Lecture Notes, 2011, 72(2011): 1-19. doi: 10.1371/journal.pone.0006098.

TAO Chao, PAN Hongbo, LI Yansheng, et al. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2438-2442. doi: 10.1109/LGRS.2015.2482520.

ZHANG Lu, MA Wenping, ZHANG Dan, et al. Stacked sparse autoencoder in PolSAR data classification using local spatial information[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(9): 1359-1363. doi: 10.1109/LGRS.2016. 2586109.

SUN Wenjun, SHAO Siyu, ZHAO Rui, et al. A sparse auto- encoder-based deep neural network approach for induction motor faults classification[J]. Measurement, 2016, 89: 171-178. doi: 10.1016/j.measurement.2016.04007.

FENG Zhipeng, LIANG Ming, CHU Fulei, et al. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 165-205. doi: 10.1016/j.ymssp.2013.01017.

施引文献

资源附件(0)

访问统计