Exploiting negative measurements for tracking star-convex extended objects

In this paper, we propose a novel approach to track extended objects by incorporating negative information. While traditional techniques to track extended targets use only positive measurements, assumed to stem from the target, the proposed estimator is also capable of incorporating negative measurements, which tell us where the target cannot be. To achieve this, we introduce a simple, robust, and easy-to-implement recursive Bayesian estimator which employs ideas from the field of curve fitting. As an application of this idea, we develop a measurement equation to estimate star-convex shapes which can be used in standard non-linear Kalman filters. Finally, we evaluate the proposed estimator using synthetic data and demonstrate its robustness in scenarios with clutter and low measurement quality.