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

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 i...

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Hauptverfasser: Perlovsky, Leonid I, Deming, Ross W
Format: Artikel
Sprache:eng
Schlagworte:
Statistics - Machine Learning
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Zusammenfassung: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.
DOI:10.48550/arxiv.1010.4236