State Estimation for a Class of Piecewise Affine State-Space Models

State Estimation for a Class of Piecewise Affine State-Space Models

We propose a filter for piecewise affine state-space models. In each filtering recursion, the true filtering posterior distribution is a mixture of truncated normal distributions. The proposed filter approximates the mixture with a single normal distribution via moment matching. The proposed algorit...

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Veröffentlicht in:IEEE signal processing letters 2017-01, Vol.24 (1), p.61-65
Hauptverfasser: Rui, Rafael, Ardeshiri, Tohid, Nurminen, Henri, Bazanella, Alexandre, Gustafsson, Fredrik
Format: Artikel
Sprache:eng
Schlagworte:
Approximation algorithms
Biological system modeling
Computational modeling
Gaussian distribution
Kalman filtering
Kalman filters
Mathematical model
nonlinear filtering
Numerical models
piecewise affine
state-space models
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Zusammenfassung:We propose a filter for piecewise affine state-space models. In each filtering recursion, the true filtering posterior distribution is a mixture of truncated normal distributions. The proposed filter approximates the mixture with a single normal distribution via moment matching. The proposed algorithm is compared with the extended Kalman filter (EKF) in a numerical simulation, where the proposed method obtains, on average, better root mean square error than the EKF.
ISSN:1070-9908
1558-2361
1558-2361
DOI:10.1109/LSP.2016.2633624