Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties
Abstract
This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inherent uncertainty in probabilistic multi-object representations. Additionally, P-GOSPA retains the interpretability of GOSPA, such as its decomposition into localization, missed detection, and false detection errors in a sound and meaningful manner. Examples and simulations are provided to demonstrate the efficacy of the proposed P-GOSPA metric.
Keywords
Cite
@article{arxiv.2412.11482,
title = {Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties},
author = {Yuxuan Xia and Ángel F. García-Fernández and Johan Karlsson and Kuo-Chu Chang and Ting Yuan and Lennart Svensson},
journal= {arXiv preprint arXiv:2412.11482},
year = {2025}
}
Comments
Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems. Code is available at https://github.com/yuhsuansia/Probabilistic-GOSPA