Low-rank Kalman filtering under model uncertainty

Shenglun Yi; Mattia Zorzi

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arXiv · 2009.02509

Low-rank Kalman filtering under model uncertainty

Shenglun Yi·Mattia Zorzi

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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.

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Shenglun Yi, Mattia Zorzi. 2020-09-05. Low-rank Kalman filtering under model uncertainty. https://arxiv.org/abs/2009.02509

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