Moving Ground Target Detection With Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution

Abstract

The main challenging task in moving ground target detection with seismic signals is the extraction of robust feature vectors for accurate localization of a seismic event. Toward this goal, a new feature extraction technique is proposed for seismic event detection based on time-frequency analysis implemented with the Smooth pseudo Wigner–Ville distribution (SPWVD). The time-frequency coefficients are utilized to constitute a Renyi entropy vector exploiting a probability distribution function. This entropy vector gives information on the localized measurement of a possible seismic event resulting from moving ground vehicles. The likelihood of an actual seismic event is then ensured by using a constant false alarm rate (CFAR) detector which dynamically adjusts the threshold to minimize false alarms. The proposed algorithm is tested on the benchmarked data set SITEX02 consisting of seismic signals generated by moving tracked and wheeled vehicles. Additionally, the algorithm is also applied on a data set generated by collecting the seismic signature of a moving wheeled vehicle, i.e., bus within the campus. The results are also compared with pseudo Wigner–Ville distribution (PWVD) and short-time Fourier transform (STFT)-based seismic event detection technique. Significantly improved detection results are observed for SPWVD technique. An F-score improvement of ~24% as well as lead time enhancement of twofold is observed in comparison with the PWVD for relatively weak seismic signals generated by wheeled Dragon Wagon (DW) vehicles. Similarly, the F-score improvement is of ~13% and the lead enhancement is approximately 10 s in comparison with the STFT method.