A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems with Missing Measurements

Document Type

Article

Publication Date

1-1-2020

Publication Title

IEEE Access

Volume

8

First Page

47522

Keywords

estimation, missing measurements, Multi-rate Kalman filter, OWA operator, sensor data fusion

Last Page

47532

Abstract

A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology.

DOI

10.1109/ACCESS.2020.2979222

E-ISSN

21693536

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