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CDC
2009
IEEE

An optimization approach to adaptive Kalman filtering

14 years 2 months ago
An optimization approach to adaptive Kalman filtering
— 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 = 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.
Maja Karasalo, Xiaoming Hu
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2009
Where CDC
Authors Maja Karasalo, Xiaoming Hu
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