Low-rank Kalman filtering under model uncertainty
Optimization and Control
2020-09-08 v1
Abstract
We consider a robust filtering problem where the nominal state space model is not reachable and different from the actual one. We propose a robust Kalman filter which solves a dynamic game: one player selects the least-favorable model in a given ambiguity set, while the other player designs the optimum filter for the least-favorable model. It turns out that the robust filter is governed by a low-rank risk sensitive-like Riccati equation. Finally, simulation results show the effectiveness of the proposed filter.
Keywords
Cite
@article{arxiv.2009.02509,
title = {Low-rank Kalman filtering under model uncertainty},
author = {Shenglun Yi and Mattia Zorzi},
journal= {arXiv preprint arXiv:2009.02509},
year = {2020}
}