In this paper, we propose the Progressive Bingham Filter (PBF), a novel stochastic filtering algorithm for nonlinear spatial orientation estimation. As an extension of the orientation filter previously proposed only for the identity measurement model based on the Bingham distribution, our method is able to handle arbitrary measurement models. Instead of the sampling- approximation scheme used in the prediction step, a closed- form solution is possible when the system equation is based on the Hamilton product. Besides stochastic approaches, we also introduce the Spherical Averaging Method (SAM), which is an application of the Riemannian averaging technique. The two approaches are then applied to a specific problem where the wavefront orientation is estimated based on Time Differences of Arrival (TDOA) and evaluated in simulations. The results show theoretical competitiveness of the PBF.
Wavefront Orientation Estimation Based on Progressive Bingham Filtering
Kailai Li, Daniel Frisch, Susanne Radtke, Benjamin Noack, and Uwe D. Hanebeck