Vehicle Classification Based on Seismic Signatures with Weighted Intrinsic Mode Functions

Abstract: Seismic signal is used for vehicle classification widely. However, this task becomes difficult as a result of various noises. To solve the problem, this paper proposes a novel de-noising algorithm which evolves from a nonparametric adaptive tool named empirical mode decomposition (EMD). EMD can decompose signals into a set of zero-mean modes called intrinsic mode functions (IMFs) that can be used to denoise a signal. Unlike other EMD-based de-noising techniques, selecting the noise-free modes to denoise signals, this paper assigns appropriate weights to the modes. In addition, considering the similarities between speech recognition and seismic vehicle classification, an algorithm scheme, consisting of improved Mel frequency cepstral coefficient (MFCC) and artificial neural network, is applied to recognize seismic signals for vehicle targets. The data from DARPA's SensIt project, which contains various seismic signatures from two different vehicle types, is used to evaluate the method. Through experiments, results demonstrate the efficacy of proposed algorithm as compared to traditional MFCC.