STATIC DATA ASSOCIATION WITH A TERRAIN-BASED PRIOR DENSITY

We consider the problem of estimating the states of a static set of ta rgets, given a collection of densities, each representing the state of a single target. We assume there is no a priori knowledge of which of the given densities represent common targets, but that a prior densit y for the target locations is available. For a two-dimensional (2-D) L ocation estimation problem, we construct a prior density model based o n known features of the terrain. We then give a simple Gaussian associ ation-estimation algorithm using the prior density and present some si mulation results. We briefly discuss extensions to nonstatic models.