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An optimization approach to adaptive Kalman filtering
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0177-1993
2011 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 47, no 8, 1785-1793 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
Adaptive filtering, Optimization, Tracking
National Category
Computational Mathematics
URN: urn:nbn:se:kth:diva-45293DOI: 10.1016/j.automatica.2011.04.004ISI: 000295344600028ScopusID: 2-s2.0-79960927828OAI: diva2:452230
Swedish Research CouncilICT - The Next Generation

QC 20111028

Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2016-05-02Bibliographically approved

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