Worst-case Prediction Performance Analysis of the Kalman Filter

Sholeh Yasini; Kristiaan Pelckmans

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arXiv · 1611.02050

Worst-case Prediction Performance Analysis of the Kalman Filter

Sholeh Yasini·Kristiaan Pelckmans

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Abstract

In this paper, we study the prediction performance of the Kalman filter (KF) in a worst-case, minimax setting as studied in online machine learning, information - and game theory. The aim is to predict the sequence of observations almost as well as the best reference predictor (comparator) sequence in a comparison class. We prove worst-case bounds on the cumulative squared prediction errors using a priori knowledge about the complexity of reference predictor sequence. In fact, the performance of the KF is derived as a function of the performance of the best reference predictor and the total amount of drift occurs in the schedule of the best comparator.

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BibTeX RIS

Sholeh Yasini, Kristiaan Pelckmans. 2016-11-07. Worst-case Prediction Performance Analysis of the Kalman Filter. https://arxiv.org/abs/1611.02050

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