Multisensory Prediction Fusion of Nonlinear Functions of the State Vector in Discrete-Time Systems

Ha Ryong Song, Il Young Song and Vladimir Shin

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 11, 249857

Abstract: We propose two new multisensory fusion predictors for an arbitrary nonlinear function of the state vector in a discrete-time linear dynamic system. Nonlinear function of the state (NFS) represents a nonlinear multivariate functional of state variables, which can indicate useful information of the target system for automatic control. To estimate the NFS using multisensory information, we propose centralized and decentralized predictors. For multivariate polynomial NFS, we propose an effective closed-form computation procedure for the predictor design. For general NFS, the most popular procedure for the predictor design is based on the unscented transformation. We demonstrate the effectiveness and estimation accuracy of the fusion predictors on theoretical and numerical examples in multisensory environment.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:11:p:249857

DOI: 10.1155/2015/249857

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