Title
Multi-sensor state estimation over lossy channels using coded measurements
Abstract
This paper focuses on a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements, and the transmission is lossy. We propose a measurement coding scheme with two goals. Firstly, it permits adjusting the communication requirements by controlling the dimension of the vector transmitted by each sensor to the central estimator. Secondly, for a given communication requirement, the scheme is optimal, within the family of linear causal coders, in the sense that the weakest channel condition is required to guarantee the stability of the estimator. For this coding scheme, we derive the minimum mean-square error (MMSE) state estimator, and state a necessary and sufficient condition with a trivial gap, for its stability. We also derive a sufficient but easily verifiable stability condition, and quantify the advantage offered by the proposed coding scheme. Finally, simulations results are presented to confirm our claims.
Year
DOI
Venue
2020
10.1016/j.automatica.2019.108561
Automatica
Keywords
Field
DocType
Networked state estimation,Sensor fusion,Packet loss,Minimum mean-square error
Lossy compression,Linear system,State estimator,Control theory,Algorithm,Communication channel,Coding (social sciences),Verifiable secret sharing,Lossy channels,Mathematics,Estimator
Journal
Volume
Issue
ISSN
111
1
0005-1098
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
Tianju Sui1235.14
Damián Marelli220.36
Xi-Ming Sun385062.94
Minyue Fu41878221.17