Paper Info
Title
Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters.
Abstract
In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.
Year
DOI
Venue
2019
10.3390/s19204436
SENSORS
Keywords
Field
DocType
RELS algorithm,correlation function method,unknown packet receiving rate,unknown noise variance,unknown model parameter,self-tuning fusion filter
Random variable,Model transformation,Identifier,Network packet,Algorithm,Optimal estimation,Electronic engineering,Self-tuning,Engineering,Parameter identification problem,Bernoulli's principle
Journal
Volume
Issue
ISSN
19
20.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Minhui Wang100.34
Shuli Sun273452.41