A Bayesian robust Kalman smoothing framework for state-space models with uncertain noise statistics

A Bayesian robust Kalman smoothing framework for state-space models with uncertain noise statistics

Abstract The classical Kalman smoother recursively estimates states over a finite time window using all observations in the window. In this paper, we assume that the parameters characterizing the second-order statistics of process and observation noise are unknown and propose an optimal Bayesian Kal...

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Bibliographic Details
Main Authors: Roozbeh Dehghannasiri, Xiaoning Qian, Edward R. Dougherty
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Kalman smoother
Robust filtering
Bayesian robustness
Innovation process
Orthogonality principle
Online Access:http://link.springer.com/article/10.1186/s13634-018-0577-1

Internet

http://link.springer.com/article/10.1186/s13634-018-0577-1

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