Performance vs complexity trade-offs for Markovian networked jump estimators
Abstract
This paper addresses the design of a state observer for networked systems with random delays and dropouts. The model of plant and network covers the cases of multiple sensors, out-of-sequence and buffered measurements. The measurement outcomes over a finite interval model the network measurement reception scenarios, which follow a Markov distribution. We present a tractable optimization problem to precalculate off-line a finite set of gains of jump observers. The proposed procedure allows us to trade the complexity of the observer implementation for achieved performance. Several examples illustrate that the online computational cost of the observer implementation is lower than that of the Kalman filter, whilst the performance is similar.
Cite
@article{arxiv.1403.5151,
title = {Performance vs complexity trade-offs for Markovian networked jump estimators},
author = {Daniel Dolz and Daniel E. Quevedo and Ignacio Peñarrocha and Roberto Sanchis},
journal= {arXiv preprint arXiv:1403.5151},
year = {2014}
}
Comments
Extended version of article to be published in the Proceedings of the 19th IFAC World Congress, 2014