KEYWORDS: Fuzzy logic, Sensor networks, Acoustics, Binary data, Detection and tracking algorithms, Sensors, Signal processing, Signal to noise ratio, Interference (communication), Fusion energy
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.
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