Title :
Unbiased minimum-variance filtering for systems with randomly multi-step sensor delays
Author :
Yilian Zhang ; Fuwen Yang ; Qing-Long Han
Author_Institution :
Key Lab. of Adv. Control & Optimization for Chem. Processes of Minist. of Educ., East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.
Keywords :
delay filters; delay systems; electric sensing devices; matrix algebra; random processes; recursive estimation; recursive filters; augmented method; delayed system; filter dimensions reduction; filter gain matrix; linear unbiased minimum variance filtering; mathematical model; randomly multistep sensor delay; recursive algorithm; state vector; Computational modeling; Covariance matrices; Delays; Kalman filters; Noise; State estimation; Kalman filters; communication networks; delay systems; filtering; networked control systems; recursive estimation; sensor system; stochastic processes;
Conference_Titel :
Industrial Electronics Society, IECON 2014 - 40th Annual Conference of the IEEE
DOI :
10.1109/IECON.2014.7049053
