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.
Highlights
With the fast development of sensor, computer, and communication technologies, multi-sensor information fusion technology has received much attention
Different from another study [22], in which correlation functions were applied for identifications of missing measurement rates and the recursive extended least squares (RELS) algorithm were applied for model parameters (MPs), in this paper, the RELS algorithm was only used for simultaneous identifications of packet receiving rates and MPs
That means that ST local filters, covariance matrix (CCM) between arbitrary two local ST filters, and ST fusion filter asymptotically converged to the corresponding optimal local filters, CCMs between arbitrary two local optimal filters, and optimal fusion filter, at least when they had identified MPs, packet receiving rates, and noise variances (NVs)
Summary
With the fast development of sensor, computer, and communication technologies, multi-sensor information fusion technology has received much attention. Until now, when missing measurement or packet dropout rates, NVs, and MPs were unknown, the corresponding ST estimation results were rarely reported since it was difficult to solve identification and ST state filters of such a complex system with so many unknown terms. (3) the correlation function was utilized for identifications of unknown NVs; (4) a ST distributed fusion state filter was proposed by applying a matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance (LUMV) sense; and (5) the convergence of the algorithms was proven.
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