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It is a mandatory requirement that communication channels use signal sampling and quantization, which introduce errors. As a result, algorithms must be devised to recover or estimate the input signals to the channels. Signal estimation introduces dynamic delays and affect feedback systems' stability and performance. Theoretically, sampling and quantization may be viewed as a means of reducing time...
For a connected network of sensors we consider deriving the linear update weights required by a 1-hop distributed linear averaging algorithm (denoted 1-DLA) such that average-consensus is reached when the sensor nodes simultaneously track, by linear stochastic approximation, a set of distinct Markov chains with time-varying regime. It is found the desired consensus is infeasible for any 1-hop 1-DLA...
This paper is concerned with a two-time-scale approximation of Wonham filters. A main feature is that the underlying hidden Markov chain has a large state space. To reduce computational complexity, we develop two-time-scale approach. Under time scale separation, we divide the state space of the Markov chain into a number of groups such that the chain jumps rapidly within each group and switches occasionally...
We develop a filtering scheme for hybrid systems with the process dictating the system configuration being a finite-state Markov chain. Exploiting hierarchical structure of the underlying system, the states of the Markov chain are divided into a number of groups so that it jumps rapidly within each group and slowly among different groups. Focusing on reduction of computational complexity, the filtering...
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