Seismic signal recognition using improved BP neural network and combined feature extraction method
来源期刊：中南大学学报(英文版)2014年第5期
论文作者：PENG Zhao-qin(彭朝琴) CAO Chun(曹纯) HUANG Jiao-ying(黄姣英) LIU Qiu-sheng(刘秋生)
文章页码：1898 - 1906
Key words：seismic signal; feature extraction; BP neural network; signal identification
Abstract: Seismic signal is generally employed in moving target monitoring due to its robust characteristic. A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network. For analyzing the seismic signal of the moving objects, the seismic signal of person and vehicle was acquisitioned from the seismic sensor, and then feature vectors were extracted with combined methods after filter processing. Finally, these features were put into the improved BP neural network designed for effective signal classification. Compared with previous ways, it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results. It also shows the effectiveness of the improved BP neural network.
PENG Zhao-qin(彭朝琴)1, CAO Chun(曹纯)1, HUANG Jiao-ying(黄姣英)2, LIU Qiu-sheng(刘秋生)3
(1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Science & Technology on Reliability & Environmental Engineering Laboratory, Beihang University,
Beijing 100191, China;
3. Ordnance Engineering College, Shijiazhuang 050003, China)
Abstract:Seismic signal is generally employed in moving target monitoring due to its robust characteristic. A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network. For analyzing the seismic signal of the moving objects, the seismic signal of person and vehicle was acquisitioned from the seismic sensor, and then feature vectors were extracted with combined methods after filter processing. Finally, these features were put into the improved BP neural network designed for effective signal classification. Compared with previous ways, it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results. It also shows the effectiveness of the improved BP neural network.
Key words:seismic signal; feature extraction; BP neural network; signal identification
