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An optimization approach to adaptive Kalman filtering
- Source :
-
Automatica . Aug2011, Vol. 47 Issue 8, p1785-1793. 9p. - Publication Year :
- 2011
-
Abstract
- Abstract: In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix by solving an optimization problem over a short window of data. The algorithm recovers the observations from a system 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00051098
- Volume :
- 47
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Automatica
- Publication Type :
- Academic Journal
- Accession number :
- 63567803
- Full Text :
- https://doi.org/10.1016/j.automatica.2011.04.004