Recursive Bayesian Filtering in Circular State Spaces
This work addresses filtering challenges in circular domains, such as robotics or navigation, but appears incremental as it builds on existing circular statistics methods.
The paper tackles the problem of recursive Bayesian filtering in circular state spaces by introducing a general framework for estimation using wrapped normal and von Mises distributions, with results showing thorough evaluation and comparison to state-of-the-art solutions.
For recursive circular filtering based on circular statistics, we introduce a general framework for estimation of a circular state based on different circular distributions, specifically the wrapped normal distribution and the von Mises distribution. We propose an estimation method for circular systems with nonlinear system and measurement functions. This is achieved by relying on efficient deterministic sampling techniques. Furthermore, we show how the calculations can be simplified in a variety of important special cases, such as systems with additive noise as well as identity system or measurement functions. We introduce several novel key components, particularly a distribution-free prediction algorithm, a new and superior formula for the multiplication of wrapped normal densities, and the ability to deal with non-additive system noise. All proposed methods are thoroughly evaluated and compared to several state-of-the-art solutions.