Title :
Wavelet denoising and feature extraction of seismic signal for footstep detection
Author :
Xing, Huai-fei ; Li, Fang ; Liu, Yu-Liang
Author_Institution :
Chinese Acad. of Sci., Beijing
Abstract :
Seismic sensors are widely used to detect moving target in ground sensor networks. Footstep detection is very important for security surveillance and other applications. Because of non-stationary characteristic of seismic signal and complex environment conditions, footstep detection is a very challenging problem. A novel wavelet denoising method based on singular value decomposition is used to solve these problems. The signal-to-noise ratio (SNR) of raw footstep signal is greatly improved using this strategy. The feature extraction method is also discussed after denoising procedure. Comparing with kurtosis statistic feature, the wavelet energy feature is more promising for seismic footstep detection, especially in a long distance surveillance.
Keywords :
feature extraction; signal denoising; signal detection; wavelet transforms; feature extraction; footstep detection; ground sensor networks; kurtosis statistic feature; long distance surveillance; security surveillance; seismic sensors; seismic signal; signal-to-noise ratio; singular value decomposition; wavelet denoising; Acoustic sensors; Acoustic signal detection; Feature extraction; Noise reduction; Sensor phenomena and characterization; Signal analysis; Signal detection; Surveillance; Wavelet analysis; Working environment noise; Wavelet denoising; feature extraction; footstep detection; seismic signal;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
DOI :
10.1109/ICWAPR.2007.4420667