A multi scan clutter density estimator

Data association attempts to discriminate between the target and the clutter measurements, usually calculating the posterior probabilities of measurement origins. The clutter (spurious) measurements are random and (we presume) follow the Poisson distribution. The Poisson distribution is non-homogeneous and is parametrized by intensity (the clutter measurement density). The clutter measurement density is almost always a priori unknown, and is often non stationary. Here we propose a measurement oriented clutter density estimator with probability hypothesis density (PHD) filtering which integrates information from single-scan spatial clutter density estimator, and can follow and smooth non-stationary clutter information.