Target tracking with range and bearing measurements via robust linear filtering

In this paper we provide a robust version of a linear Kalman filter for target tracking with nonlinear range and bearing measurements. Moreover, we prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement based linear filtering.