Open AccessSuboptimal Filtering of Networked Discrete-Time Systems with Random Observation Losses
Author(s) -
Shouwan Gao,
Pengpeng Chen
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/151836
Subject(s) - independent and identically distributed random variables , network packet , bernoulli's principle , covariance , filter (signal processing) , channel (broadcasting) , control theory (sociology) , convergence (economics) , stability (learning theory) , discrete time and continuous time , mimo , mathematics , bernoulli process , computer science , minimum mean square error , process (computing) , packet loss , algorithm , random variable , statistics , telecommunications , engineering , computer network , artificial intelligence , estimator , economic growth , aerospace engineering , operating system , control (management) , machine learning , computer vision , economics
This paper studies the remote filtering problem over a packet-dropping network. A general multiple-input-multiple-output (MIMO) discrete-time system is considered. The multiple measurements are sent over different communication channels every time step, and the packet loss phenomenon in every communication channel is described by an independent and identically distributed (i.i.d) Bernoulli process. A suboptimal filter is obtained which can minimize the mean squared estimation error. The convergence properties of the estimation error covariance are studied, and mean square stability of the suboptimal filter is proved under standard assumptions. A simulation example is exploited to demonstrate the effectiveness of the results
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