Journal
INFORMATION FUSION
Volume 34, Issue -, Pages 70-79Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.inffus.2016.06.008
Keywords
Sensor networks; Distributed filtering; Random parameter matrices; Correlated noises; Random packet dropouts
Funding
- Ministerio de Economia y Competitividad
- Fondo Europeo de Desarrollo Regional FEDER [MTM2014-52291-P]
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This paper addresses the distributed fusion filtering problem for discrete-time random signals from measured outputs perturbed by random parameter Matrices and correlated additive noises. These measurements are obtained by a sensor network with a given topology, where random packet dropouts occur during the data transmission through the different network communication channels. The distributed fusion estimation is accomplished in two phases. Firstly, by an innovation approach and using the last observation that successfully arrived if a packet is lost, a preliminary distributed least-squares estimator is designed at each sensor node using its own measurements and those from its neighbors. Secondly, every sensor collects the preliminary filters that are successfully received from its neighbors and fuses this information with its own one to generate the least-squares linear matrix-weighted distributed fusion estimator. The accuracy of the proposed estimators, which is measured by the estimation error covariances, is examined by a numerical simulation example. (C) 2016 Elsevier B.V. All rights reserved.
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