Petrov, Nikolay and Gning, Amadou and Mihaylova, Lyudmila and Angelova, D (2012) Box Particle Filtering for Extended Object Tracking. In: Information Fusion (FUSION), 2012 15th International Conference on :. IEEE, pp. 82-89. ISBN 978-1-4673-0417-7Full text not available from this repository.
This paper focuses on real-time tracking of an extended object in the presence of clutter. This task reduces to the estimation of the object kinematic state and its extent, based on multiple measurements originated from the same object. A solution to this challenging problem is presented within the recently proposed Box Particle Filtering framework. The Box Particle Filter replaces the point samples with regions, which we call boxes. The performance of the Box Particle Filter for extended object tracking is studied over a challenging scenario with simulated cluttered radar measurements, consisting of range and bearing components. The efficiency is evaluated for different levels of clutter, number of box particles, uncertainty regions for the measurements, number of the active sensors collecting the measurements data and iterations for the contraction of the uncertainty region. Accurate estimation results are demonstrated.
|Item Type:||Contribution in Book/Report/Proceedings|
|Uncontrolled Keywords:||particle filtering ; extended objects ; Box Particle filters ; nonlinear systems ; state and parameter estimation|
|Subjects:||?? qa75 ??|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||27 Jul 2012 10:03|
|Last Modified:||01 May 2017 01:57|
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