A particle filter for target arrival detection and tracking in Track-Before-Detect

Author(s):  
Alexandre Lepoutre ◽  
Olivier Rabaste ◽  
Francois Le Gland
2016 ◽  
Vol 30 (13) ◽  
pp. 877-888 ◽  
Author(s):  
Nagisa Koyama ◽  
Ryosuke Tajima ◽  
Noriaki Hirose ◽  
Kazutoshi Sukigara

2011 ◽  
Vol 30 (4) ◽  
pp. 941-944 ◽  
Author(s):  
Ya-xin Gong ◽  
Hong-wen Yang ◽  
Wei-dong Hu ◽  
Wen-xian Yu

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1577 ◽  
Author(s):  
Bo Yan ◽  
Xu Yang Zhao ◽  
Na Xu ◽  
Yu Chen ◽  
Wen Bo Zhao

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.


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