On-Line Bayesian Classifier Design for Measurement Fusion

Li Wei Fong; Pi Ching Lou; Kung Ting Lin

doi:10.4028/www.scientific.net/AMR.461.826

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 461On-Line Bayesian Classifier Design for Measurement...

On-Line Bayesian Classifier Design for Measurement Fusion

Abstract:

A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Network (RBFN). By incorporating Markov chain into Bayesian estimation scheme, a RBFN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is developed that can handle the switching plant in the multi-sensor environment. The simulation results are presented which demonstrate the effectiveness of the proposed method.

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Periodical:

Advanced Materials Research (Volume 461)

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826-829

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.461.826

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Online since:

February 2012

Authors:

Li Wei Fong, Pi Ching Lou, Kung Ting Lin

Keywords:

Adaptive Kalman Filtering, Measurement Fusion, Probabilistic Neural Network, Radial Basis Function Network

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