Worst-case Prediction Performance Analysis of the Kalman Filter
Optimization and Control
2016-11-23 v2
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.
Keywords
Cite
@article{arxiv.1611.02050,
title = {Worst-case Prediction Performance Analysis of the Kalman Filter},
author = {Sholeh Yasini and Kristiaan Pelckmans},
journal= {arXiv preprint arXiv:1611.02050},
year = {2016}
}