Asynchronous distributed state estimation for continuous-time stochastic processes

Zdzisław Kowalczuk; Mariusz Domżalski

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 2, page 327-339
  • ISSN: 1641-876X

Abstract

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The problem of state estimation of a continuous-time stochastic process using an Asynchronous Distributed multi-sensor Estimation (ADE) system is considered. The state of a process of interest is estimated by a group of local estimators constituting the proposed ADE system. Each estimator is based, e.g., on a Kalman filter and performs single sensor filtration and fusion of its local results with the results from other/remote processors to compute possibly the best state estimates. In performing data fusion, however, two important issues need to be addressed namely, the problem of asynchronism of local processors and the issue of unknown correlation between asynchronous data in local processors. Both the problems, along with their solutions, are investigated in this paper. Possible applications and effectiveness of the proposed ADE approach are illustrated by simulated experiments, including a non-complete connection graph of such a distributed estimation system.

How to cite

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Zdzisław Kowalczuk, and Mariusz Domżalski. "Asynchronous distributed state estimation for continuous-time stochastic processes." International Journal of Applied Mathematics and Computer Science 23.2 (2013): 327-339. <http://eudml.org/doc/257119>.

@article{ZdzisławKowalczuk2013,
abstract = {The problem of state estimation of a continuous-time stochastic process using an Asynchronous Distributed multi-sensor Estimation (ADE) system is considered. The state of a process of interest is estimated by a group of local estimators constituting the proposed ADE system. Each estimator is based, e.g., on a Kalman filter and performs single sensor filtration and fusion of its local results with the results from other/remote processors to compute possibly the best state estimates. In performing data fusion, however, two important issues need to be addressed namely, the problem of asynchronism of local processors and the issue of unknown correlation between asynchronous data in local processors. Both the problems, along with their solutions, are investigated in this paper. Possible applications and effectiveness of the proposed ADE approach are illustrated by simulated experiments, including a non-complete connection graph of such a distributed estimation system.},
author = {Zdzisław Kowalczuk, Mariusz Domżalski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {continuous-time stochastic processes; distributed systems; state estimation; Kalman filtering},
language = {eng},
number = {2},
pages = {327-339},
title = {Asynchronous distributed state estimation for continuous-time stochastic processes},
url = {http://eudml.org/doc/257119},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Zdzisław Kowalczuk
AU - Mariusz Domżalski
TI - Asynchronous distributed state estimation for continuous-time stochastic processes
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 2
SP - 327
EP - 339
AB - The problem of state estimation of a continuous-time stochastic process using an Asynchronous Distributed multi-sensor Estimation (ADE) system is considered. The state of a process of interest is estimated by a group of local estimators constituting the proposed ADE system. Each estimator is based, e.g., on a Kalman filter and performs single sensor filtration and fusion of its local results with the results from other/remote processors to compute possibly the best state estimates. In performing data fusion, however, two important issues need to be addressed namely, the problem of asynchronism of local processors and the issue of unknown correlation between asynchronous data in local processors. Both the problems, along with their solutions, are investigated in this paper. Possible applications and effectiveness of the proposed ADE approach are illustrated by simulated experiments, including a non-complete connection graph of such a distributed estimation system.
LA - eng
KW - continuous-time stochastic processes; distributed systems; state estimation; Kalman filtering
UR - http://eudml.org/doc/257119
ER -

References

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