Sequential Monte Carlo framework for extended object tracking

2005 ◽  
Vol 152 (5) ◽  
pp. 353 ◽  
Author(s):  
J. Vermaak ◽  
N. Ikoma ◽  
S.J. Godsill
Keyword(s):  
Monte Carlo ◽  
Object Tracking ◽  
Sequential Monte Carlo ◽  
Extended Object ◽  
Monte Carlo Framework

scholarly journals Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

2014 ◽  
Vol 25 ◽  
pp. 1-16 ◽  
Author(s):  
Lyudmila Mihaylova ◽  
Avishy Y. Carmi ◽  
François Septier ◽  
Amadou Gning ◽  
Sze Kim Pang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Object Tracking ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Sequential Monte Carlo Methods ◽  
Extended Object

scholarly journals Multiellipsoidal extended target tracking with known extent using sequential Monte Carlo framework

2019 ◽  
pp. 1546-1558
Author(s):  
SÜLEYMAN FATİH KARA ◽  
EMRE ÖZKAN
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Target Surface ◽  
Extended Target ◽  
Discrete States ◽  
Independent Association ◽  
Correct Association ◽  
Monte Carlo Framework

In this paper, we consider a variant of the extended target tracking (ETT) problem, namely the multiel- lipsoidal ETT problem. In multiellipsoidal ETT, target extent is represented by multiple ellipses, which correspond to the origin of the measurements on the target surface. The problem involves estimating the target’s kinematic state and solving the association problem between the measurements and the ellipses. We cast the problem in a sequential Monte Carlo (SMC) framework and investigate different marginalization strategies to find an efficient particle filter. Under the known extent assumption, we define association variables to find the correct association between the measurements and the ellipses; hence, the posterior involves both discrete and continuous random variables. By expressing the measurement likelihood as a mixture of Gaussians we derive and employ a marginalized particle filter for the independent association variables without sampling the discrete states. We compare the performance of the method with its alternatives and illustrate the gain in nonstandard marginalization.

scholarly journals Extended Object Tracking Using Monte Carlo Methods

2008 ◽  
Vol 56 (2) ◽  
pp. 825-832 ◽  
Author(s):  
Donka Angelova ◽  
Lyudmila Mihaylova
Keyword(s):  
Monte Carlo ◽  
Object Tracking ◽  
Monte Carlo Methods ◽  
Extended Object
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