Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity

Details Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity

2010-10-20

arxiv.org/abs/1010.4236v1

Statistics

Machine Learning

13 pages

Scientific paper

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.

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