Multi-object filtering with Poisson arrival-rate measurements
Daniel E. Clark, Sharad Nagappa · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Recent interest in multi-object filtering has focussed on the problem of discrete-time filtering, where sets of measurements are collected at regular intervals from the sensor. Many sensors do not provide multiple measurements at regular intervals but instead provide single-measurement reports at irregular time-steps. In this paper we study the multi-object filtering problem for estimation from measurements where the target and clutter processes provide measurements with Poisson arrival rates. In particular, we show that the Probability Hypothesis Density (PHD) filter can be adapted to Poisson arrival rate measurements by modelling the probability of detection with an exponential distribution. We demonstrate the approach in simulated scenarios.