Paper Info
Title
Self-Tuning Distributed Fusion Filter for Multi-Sensor Systems Subject to Unknown Model Parameters and Missing Measurement Rates.
Abstract
A self-tuning fusion estimation problem is addressed for multi-sensor (MS) linear discrete-time stochastic systems subject to unknown model parameters (UMPs) and missing measurement rates. The phenomena of missing measurements for different sensors are described by random variable sequences obeying Bernoulli distributions. The UMPs and missing measurement rates are identified online by the recursive extended least squares (RELS) algorithms and correlation functions, respectively. A distributed fusion identifier for UMPs is presented by using matrix-weighted fusion estimation (MWEE) algorithm in the linear unbiased minimum variance sense. Furthermore, the corresponding self-tuning state estimation algorithms are obtained by substituting the identified model parameters and missing measurement rates into the local optimal filters, cross-covariance matrices (CCMs), and distribution optimal fusion filter. Finally, the convergence of the presented algorithms is analyzed. A numerical example shows the effectiveness of the presented algorithms.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2874444
IEEE ACCESS
Keywords
Field
DocType
RELS,correlation function,multi-sensor system,unknown model parameter,unknown missing measurement rate,self-tuning fusion filter
Convergence (routing),Minimum-variance unbiased estimator,Random variable,Identifier,Matrix (mathematics),Computer science,Fusion,Algorithm,Self-tuning,Bernoulli's principle,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Guang-Quan Duan100.34
Shuli Sun273452.41