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arXiv · 2309.07631

Unified Linearization-based Nonlinear Filtering

Anton Kullberg·Isaac Skog·Gustaf Hendeby
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Abstract

This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterated unscented Kalman filter; and dynamically iterated filters, such as the dynamically iterated posterior linearization filters; can be unified in terms of a general algorithm. The general algorithm highlights the strong similarities between specific filtering algorithms in the three filter classes and facilitates an in-depth understanding of the pros and cons of the different filter classes and algorithms. We end with a numerical example showing the estimation accuracy differences between the three classes of filters when applied to a nonlinear localization problem.

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Anton Kullberg, Isaac Skog, Gustaf Hendeby. 2023-09-14. Unified Linearization-based Nonlinear Filtering. https://arxiv.org/abs/2309.07631

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