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Title: Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Authors: Hu, J
Wang, Z
Gao, H
Stergioulas, LK
Keywords: Nonlinear systems;Extended Kalman filter;Stochastic nonlinearities;Multiple missing measurements;Recursive filter;Riccati-like difference equation
Issue Date: 2012
Publisher: Elsevier
Citation: Automatica, 48(9): 2007 - 2015, Sep 2012
Abstract: In this paper, the extended Kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0,1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0,1] with known conditional probability. The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.
Description: Copyright @ 2012 Elsevier
ISSN: 0005-1098
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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