Static data association with a terrain-based prior density

Abstract

We consider the problem of estimating the states of a static set of targets, 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 density for the target locations is available. For a two-dimensional (2-D) location estimation problem, we construct a prior density model based on known features of the terrain. We then give a simple Gaussian association-estimation algorithm using the prior density and present some simulation results. We briefly discuss extensions to nonstatic models.