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

2012 ◽  
Author(s):  
Alexandre Lepoutre ◽  
Olivier Rabaste ◽  
Francois Le Gland
Keyword(s):  
Particle Filter ◽  
Detection And Tracking ◽  
Track Before Detect

IR tag detection and tracking with omnidirectional camera using track-before-detect particle filter

2016 ◽  
Vol 30 (13) ◽  
pp. 877-888 ◽  
Author(s):  
Nagisa Koyama ◽  
Ryosuke Tajima ◽  
Noriaki Hirose ◽  
Kazutoshi Sukigara
Keyword(s):  
Particle Filter ◽  
Omnidirectional Camera ◽  
Detection And Tracking ◽  
Track Before Detect

Multiple Model Particle Filter Based Track-before-Detect for Maneuvering Weak Target

2011 ◽  
Vol 30 (4) ◽  
pp. 941-944 ◽  
Author(s):  
Ya-xin Gong ◽  
Hong-wen Yang ◽  
Wei-dong Hu ◽  
Wen-xian Yu
Keyword(s):  
Particle Filter ◽  
Multiple Model ◽  
Model Particle ◽  
Track Before Detect ◽  
Weak Target

scholarly journals A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1577 ◽  
Author(s):  
Bo Yan ◽  
Xu Yang Zhao ◽  
Na Xu ◽  
Yu Chen ◽  
Wen Bo Zhao
Keyword(s):  
Target Detection ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Real Data ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Detection And Tracking ◽  
Extended Target ◽  
Air Surveillance ◽  
Track Before Detect

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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