EM approach for tracking star-convex extended objects
Hauke Kaulbersch, Marcus Baum, Peter Willett · 2017
We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization method that allows for a closed-form EM iteration. The performance of the EM approach is demonstrated in comparison with a recursive Gaussian filter based on the Random Hypersurface Model (RHM).