- Main
A Comprehensive Analysis of Tracking as a Data Association Problem
- Poblenz, Eric Charles
- Advisor(s): Elkaim, Gabriel H
Abstract
Algorithms based on traditional notion of tracking as a state estimation problem
yield just a single interpretation of the data. For some applications, the ability to
identify ambiguities and compare different interpretations using a well-defined measure
of confidence is critical. Such applications require a direct solution to the data association
problem in order to characterize the relevant uncertainty. This notion of tracking
has received relatively little attention largely due to a failure to recognize its utility
beyond maintaining the state estimation process. As a result, the options available to
the practitioner are limited and the performance of statistical data association models
is not well understood, especially in terms of the quality of the sample they produce.
This work has sought to change that by developing a new data association
model that extends the scope and flexibility of existing models. The questions of how
to specify an objective prior distribution over data association hypotheses and how
to efficiently perform inference on the high-dimensional posterior distribution are very
much open. To help provide answers, we considered numerous different priors, including
Bayesian nonparametric models and several models never before applied to tracking.
With regard to inference, we considered various implementations of Markov chain Monte
Carlo (MCMC) and population Monte Carlo (PMC) samplers. A comprehensive evaluation
was performed in the context of a wide-area radar surveillance application.
Main Content
Enter the password to open this PDF file:
-
-
-
-
-
-
-
-
-
-
-
-
-
-