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

Ha Ryong Song | Zendy; Il Young Song | Zendy; Vladimir Shin | Zendy

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

Author(s) -

Ha Ryong Song,

Il Young Song,

Vladimir Shin

Publication year - 2015

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2015/249857

Subject(s) - computer science , nonlinear system , computation , state vector , transformation (genetics) , state (computer science) , polynomial , function (biology) , multivariate statistics , fusion , artificial intelligence , control theory (sociology) , algorithm , machine learning , control (management) , mathematics , mathematical analysis , biochemistry , chemistry , physics , linguistics , philosophy , quantum mechanics , classical mechanics , evolutionary biology , biology , gene

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.

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