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

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.

preprint2010arXivOpen access
Leonid I. PerlovskyRoss W. Deming
Machine Learning
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Open access2 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Leonid I. PerlovskyRoss W. Deming

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