Multiple Sensor Bayesian Extended Target Tracking Fusion Approaches Using Random Matrices

The tracking of extended targets is attracting a growing literature thanks to the high resolution of several modern radar systems. A fully Bayesian solution has been proposed in the random matrix framework. In this paper, the fusion of detections acquired by multiple sensors is analyzed. Four different methods are proposed to track and to estimate jointly both the kinematic and extent parameters. All of them use the same multi-sensor kinematic vector measurement update. The first approach is based on a particle approximation of the extent state probability density function, whereas the other three are based on an inverse Wishart representation of the latter. Extensive simulations evaluate the performance of the different approaches. The best performance is obtained by the particle filter-based approach paid by an increased computational burden. Comparable performance are observed for the two updates based on multi-sensor generalization, while the worst performance is obtained by the updated based on fusion approximation.