An Optimization Approach to Adaptive Kalman Filtering
| Autor: | Xiaoming Hu, Maja Karasalo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Optimization problem
Noise (signal processing) Covariance matrix Beräkningsmatematik Computational mathematics Kalman filter Kinematics Adaptive filtering tracking Tracking (particle physics) System dynamics Nonlinear system Computational Mathematics Control and Systems Engineering Control theory A priori and a posteriori Electrical and Electronic Engineering optimization Mathematics |
| Zdroj: | CDC |
| Popis: | 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. Uppdaterad till från manuskript till konferensbidrag: 20100722 QC 20100722 |
| Databáze: | OpenAIRE |
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