摘要
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.
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.
基金
Project(61201028)supported by the National Natural Science Foundation of China
Project(YWF-12-JFGF-060)supported by the Fundamental Research Funds for the Central Universities,China
Project(2011ZD51048)supported by Aviation Science Foundation of China
