Self-Tuning Distributed Fusion Filter for Multi-Sensor Systems Subject to Unknown Model Parameters and Missing Measurement Rates

• Published in: IEEE access Vol. 6; pp. 61519 - 61528
• Main Authors: Duan, Guang-Quan, Sun, Shu-Li
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Correlation; correlation function; Covariance matrix; Delays; Discrete time systems; Estimation; Filtering algorithms; Filtering theory; Information filters; Mathematical models; Matrix methods; multi-sensor system; Parameter identification; Random variables; RELS; Self tuning; self-tuning fusion filter; State estimation; Stochastic systems; unknown missing measurement rate; unknown model parameter
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2874444

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 (MWFE) 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.