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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| creator | Perlovsky, Leonid I Deming, Ross W |
| description | 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_str_mv | 10.48550/arxiv.1010.4236 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.1010.4236</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Machine Learning |
| title | Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity |
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