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Proceedings Paper

An improved DS acoustic-seismic modality fusion algorithm based on a new cascaded fuzzy classifier for ground-moving targets classification in wireless sensor networks
Author(s): Qiang Pan; Jianming Wei; Hongbing Cao; Na Li; Haitao Liu
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Paper Abstract

A new cascaded fuzzy classifier (CFC) is proposed to implement ground-moving targets classification tasks locally at sensor nodes in wireless sensor networks (WSN). The CFC is composed of three and two binary fuzzy classifiers (BFC) respectively in seismic and acoustic signal channel in order to classify person, Light-wheeled (LW) Vehicle, and Heavywheeled (HW) Vehicle in presence of environmental background noise. Base on the CFC, a new basic belief assignment (bba) function is defined for each component BFC to give out a piece of evidence instead of a hard decision label. An evidence generator is used to synthesize available evidences from BFCs into channel evidences and channel evidences are further temporal-fused. Finally, acoustic-seismic modality fusion using Dempster-Shafer method is performed. Our implementation gives significantly better performance than the implementation with majority-voting fusion method through leave-one-out experiments.

Paper Details

Date Published: 7 May 2007
PDF: 8 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670Y (7 May 2007); doi: 10.1117/12.719239
Show Author Affiliations
Qiang Pan, Shanghai Institute of Microsystem and Information Technology (China)
Jianming Wei, Shanghai Institute of Microsystem and Information Technology (China)
Hongbing Cao, Shanghai Institute of Microsystem and Information Technology (China)
Na Li, Shanghai Institute of Microsystem and Information Technology (China)
Haitao Liu, Shanghai Institute of Microsystem and Information Technology (China)


Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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