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

10/20/2010

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by Leonid I. Perlovsky, et al.

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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.

Leonid I. Perlovsky
1 publication
Ross W. Deming
1 publication

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