Gaussian target tracking with direction-of-arrival von Mises–Fisher measurements

This paper proposes a novel algorithm for target tracking with direction-of-arrival measurements, modeled by von Mises-Fisher distributions. The algorithm makes use of the assumed density framework with Gaussian distributions, in which the posterior probability density of the target state is approximated by a Gaussian density. A key component of this algorithm is that the proposed Bayesian model of the measurements takes into account the specific characteristics of angular measurements by using a von Mises-Fisher distribution. We propose two implementations of the algorithm, one based on first-order Taylor series expansion and another one based on sigma points. Simulation results show the benefits of the proposed algorithms in relation to other Gaussian filters in the literature.