Static Data Association with a Terrain-Based Prior Density

Report
Authors:Barker, Allen, Institute for Parallel ComputationUniversity of Virginia Brown, Donald, Institute for Parallel ComputationUniversity of Virginia Martin, Worthy, Institute for Parallel ComputationUniversity of Virginia
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 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.

Rights:
All rights reserved (no additional license for public reuse)
Language:
English
Source Citation:

Barker, Allen, Donald Brown, and Worthy Martin. "Static Data Association with a Terrain-Based Prior Density." University of Virginia Institute for Parallel Computation Tech Report (1994).

Publisher:
University of Virginia, Institute for Parallel Computation
Published Date:
1994