An Optimization Approach to Adaptive Kalman Filtering
2009 (English)In: 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009; Shanghai; 15 December 2009 through 18 December 2009; Category number 09CH38112; Code 79678, 2009, 2333-2338 p.Conference paper (Refereed)
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 shortwindow of data. The algorithm recovers the observations h(x) from a system dot x = 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 isdemonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics.Simulations indicate superiority overa standard MMAE algorithm for a large class of systems.
Place, publisher, year, edition, pages
2009. 2333-2338 p.
Adaptive filtering, optimization, tracking
IdentifiersURN: urn:nbn:se:kth:diva-11145DOI: 10.1109/CDC.2009.5400877ISI: 000336893602136ScopusID: 2-s2.0-77950856267OAI: oai:DiVA.org:kth-11145DiVA: diva2:236373
Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC), Shanghai, PEOPLES R CHINA
Uppdaterad till från manuskript till konferensbidrag: 20100722 QC 201007222009-10-012009-09-222015-06-10Bibliographically approved