An optimization approach to adaptive Kalman filtering
2011 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 47, no 8, 1785-1793 p.Article in journal (Refereed) Published
In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a short window of data. The algorithm recovers the observations h(x) from a system (x) over dot = f (x), y = h(x) + v without a priori knowledge of system dynamics. Potential applications include target tracking using a network of nonlinear sensors, servoing, mapping, and localization. The algorithm is demonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics. Simulations indicate superiority over a standard MMAE algorithm for a large class of systems.
Place, publisher, year, edition, pages
Elsevier, 2011. Vol. 47, no 8, 1785-1793 p.
Adaptive filtering, Optimization, Tracking
IdentifiersURN: urn:nbn:se:kth:diva-45293DOI: 10.1016/j.automatica.2011.04.004ISI: 000295344600028ScopusID: 2-s2.0-79960927828OAI: oai:DiVA.org:kth-45293DiVA: diva2:452230
FunderSwedish Research CouncilICT - The Next Generation
QC 201110282011-10-282011-10-282016-05-02Bibliographically approved