  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.
