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Maximum Likelihood Joint Tracking and Association in Strong Clutter : Massima somiglianza per il tracciamento dei giunti e associazioni in grosse differenze, in: International Journal of Advanced Robotic Systems
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Autore Perlovsky, Leonid I.; Deming, Ross W.
Pubblicato  InTech Open Access Publisher, 2013
edizione  
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Abstract We have developed a maximum likelihood
formulation for a joint detection, tracking and association
problem. An efficient non‐combinatorial algorithm for
this problem is developed in case of strong clutter for
radar data. By using an iterative procedure of the
dynamic logic process “from vague‐to‐crisp” explained in
the paper, the new tracker overcomes the combinatorial
complexity of tracking in highly‐cluttered scenarios and
results in an orders‐of‐magnitude improvement in signalto‐
clutter ratio.
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Articoli a Rivista
2000 ed oltre
Superordinate work
 
no fulltext found International Journal of Advanced Robotic Systems
Autore: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Pubblicato: 2004
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Documents: International Journal of Advanced Robotic Systems
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Time of publication 2013
License information Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License

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