Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity
Machine Learning
2010-10-21 v1
Abstract
We have developed an efficient algorithm for the maximum likelihood joint tracking and association problem in a strong clutter for GMTI data. By using an iterative procedure of the dynamic logic process "from vague-to-crisp," the new tracker overcomes combinatorial complexity of tracking in highly-cluttered scenarios and results in a significant improvement in signal-to-clutter ratio.
Cite
@article{arxiv.1010.4236,
title = {Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity},
author = {Leonid I. Perlovsky and Ross W. Deming},
journal= {arXiv preprint arXiv:1010.4236},
year = {2010}
}
Comments
13 pages