A basic smart linear Kalman filter with online performance evaluation based on observable degree

Quanbo Ge, Jinyan Ma, Hongli He, Hong Li and Guoqiang Zhang

Applied Mathematics and Computation, 2020, vol. 367, issue C

Abstract: The observable degree can be used to directly explain the system filtering performance (or filtering accuracy) of Kalman filtering (KF) to some extent. The effective observable degree can not only be obtained before filtering but also be used to measure the system filtering performance. In applications, the exact knowledge of the system parameters and models is always unavailable. A basic smart Kalman filter (SKF) with online performance evaluation is proposed based on the observable degree in this paper. Since the collection of observations is limited in initial alignment with complex situations, mobile sensor networks are introduced. To improve the filtering performance with inaccuracy system parameters, the relatively optimal smart adjusting factor is iteratively selected by an optimized observable degree with autonomous learning function. The self-assessment function is also available for real-time performance evaluation. Finally, simulation examples are demonstrated to validate the proposed smart Kalman filter.

Keywords: Observable degree; Performance evaluation; Adjusting factor; Optimization; Smart Kalman filter (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:367:y:2020:i:c:s0096300319305958

DOI: 10.1016/j.amc.2019.124603

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