Multiple Sensor Measurement Updates for the Extended Target Tracking Random Matrix Model

In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state probability density function and one update based on a Rao-Blackwellized (RB) particle approximation of the state density. An extensive simulation study shows that the RB particle approach shows best performance, at the price of higher computational cost, compared to parametric approximations.