A new probabilistic data association filter based on composite expanding and fading memory polynomial filters

This paper presents the use of composite expanding and fading memory polynomial filters performing tracking in conditions of heavy clutter and low probability of detection. The composite expanding and fading memory polynomial filters are modified to incorporate probabilistic data association, and a simulation study shows that this new type of filtering offers performance comparable to the linear Kalman filter in a high clutter density and low detection probability environment.