A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Autor:	Xiaobing Yuan, Guanjun Tong, Qianwei Zhou, Dongfeng Xie, Baoqing Li
Rok vydání:	2012
Předmět:	business.industry Applied Mathematics Speech recognition Feature extraction Geophone Pattern recognition Seismic wave Signal Processing Mel-frequency cepstrum Artificial intelligence Electrical and Electronic Engineering business Classifier (UML) Feature extraction algorithm
Zdroj:	IEEE Signal Processing Letters. 19:639-642
ISSN:	1558-2361 1070-9908
DOI:	10.1109/lsp.2012.2209870
Popis:	Seismic signal is widely used in ground target classification due to its inherent characteristics. However, its propagation is highly dependent on local underlying geology. It means that nearly every one geographical environment requires a unique classifier. To resolve the problem, this paper presents a robust feature extraction method Log-Sigmoid Frequency Cepstral Coefficients (LSFCC) which evolves from Mel frequency cepstral coefficients (MFCC) for ground target classification by means of geophone. With the LSFCCs, the average classification accuracy of tracked and wheeled vehicle is more than 89% in three different geographical environments by only one classifier which is trained in one of the three environments.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::39ec436768855d3cc3365a908f4dc69c https://doi.org/10.1109/lsp.2012.2209870 Zobrazit plný text záznamu