Vehicle Classification Based on Seismic Signatures with Weighted Intrinsic Mode Functions

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 deco...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Author: Jin, Guozheng
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Artificial neural networks
Classification
Neural networks
Noise reduction
Seismic analysis
Signatures
Speech recognition
Online Access:Get full text
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Summary: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.
ISSN:2331-8422