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

doi:10.11999/JEIT160031

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Abstract

A novel recognition algorithm for Low Probability of Intercept (LPI) radar signal based on deep learning of radar signals’ Bispectra Diagonal Slice (BDS) is proposed in this paper. Firstly, a Deep Belief Network (DBN) model is established on stacked Restricted Boltzmann Machines (RBM), then the model is used for layer-by-layer unsupervised greedy learning of radar signals’ BDS. Secondly, a Back Propagation (BP) algorithm is applied to fine tune parameters of DBN model with a supervised way according to learning error. Finally, the BDS-DBN model is constructed to classify and recognize unknown LPI signals. The theoretical analysis and the simulation results show that, the average recognition accuracy of the proposed algorithm for Frequency Modulation Continuous Wave (FMCW), Frank, Costas and FSK/PSK signals can reach 93.4% or ever higher while the SNR is better than 8 dB, which is better than that of Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm and Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm.

Key words： Low Probability of Intercept (LPI) radar Deep learning Deep Belief Network (DBN) Bispectra Diagonal Slice (BDS) Restricted Boltzmann Machine (RBM)

Received: 16 January 2016 Published: 30 September 2016

PACS:

TN97

Fund:

The National Natural Science Foundation of China (61372167), The Aeronautical Science Foundation of China (20152096019)

Corresponding Authors: WANG Xing E-mail: wang_xing1965@163.com

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	WANG Xing
	ZHOU Yipeng
	ZHOU Dongqing
	CHEN Zhonghui
	TIAN Yuanrong

Cite this article:

WANG Xing,ZHOU Yipeng,ZHOU Dongqing等. Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice[J]. JEIT, 2016, 38(11): 2972-2976.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160031 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2972

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