Two Fusion Predictors for Multisensor Discretetime Linear System

Song, Ha Ryong; Jeon, Moongu; Lee, Y. S.; Choi, Tae‐Sun; Shin, Vladimir

doi:10.2316/journal.206.2009.4.206-3233

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2015

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“…The aforementioned papers [5–13] have not focused on the prediction problem, but most of them have considered only decentralized filtering of state variables in multisensory dynamic models. The decentralized prediction of the state requires special algorithms presented in [14, 15].…”

Section: Introductionmentioning

confidence: 99%

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

Song

Shin

2015

International Journal of Distributed Sensor Networks

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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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