Please use this identifier to cite or link to this item:
Title: Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays
Authors: Hu, J
Wang, Z
Shen, B
Gao, H
Keywords: Algorithm design and analysis;Delay;Educational institutions;Noise;Probabilistic logic;Random variables;Stochastic processes
Issue Date: 2013
Publisher: IEEE
Citation: IEEE Transactions on Signal Processing, 61(5): 1230 - 1238, Mar 2013
Abstract: This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.
Description: This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2013 IEEE.
ISSN: 1053-587X
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf201.42 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.