A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking
Abstract
In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.
Keywords
Cite
@article{arxiv.2406.10389,
title = {A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking},
author = {Oğul Can Yurdakul and Mehmet Çetinkaya and Enescan Çelebi and Emre Özkan},
journal= {arXiv preprint arXiv:2406.10389},
year = {2025}
}
Comments
10 pages, 6 figures, accepted by the 27th International Conference on Information Fusion (FUSION 2024). This version introduces equation (23) with the necessary modifications. The scaling factor is chosen to yield the same results