Integrating mapping and navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998.Includes bibliographical references (p. 221-232).by Christopher Michael Smith.Ph.D

Static Data Association with a Terrain-Based Prior Density

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 location estimation problem we construct a prior density model based on known features of the terrain. For a simple Gaussian association-estimation algorithm using a prior density we consider when the prior is most effective in data association, or correlation, and when it is most effective in state estimation. We present some simulation results and discuss some issues involved in measuring algorithm performance and in the algorithm implementation. We briefly discuss extensions to higher dimensional state spaces and non-static models. 1 Introduction In this paper we consider the static data association problem as a special case of the more general..