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.