Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts

DOI

10.2991/mmebc-16.2016.423How to use a DOI?

Keywords

Filter; Networked System; Unknown Interference; Packet Dropout; Linear Unbiased Minimum Variance

Abstract

This paper is concerned with the design of distributed fusion filter for networked systems with unknown measurement interferences and packet dropouts. A Bernoulli distributed random variable is used to depict the phenomenon of packet dropouts. Without any prior information about the interference, a recursive Kalman-type state filter independent of the unknown interferences is designed for each sensor subsystem by applying the linear unbiased minimum variance estimation criterion. Based on the state filters of individual subsystems, the estimation error cross-covariance matrices between any two subsystems are derived. Then, the distributed fusion filter is designed by using the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Qi
AU  - Shuli Sun
PY  - 2016/06
DA  - 2016/06
TI  - Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 2113
EP  - 2118
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.423
DO  - 10.2991/mmebc-16.2016.423
ID  - Qi2016/06
ER  -