Work

Probabilistic GOSPA: A Metric for Performance Evaluation of Multiobject Filters With Uncertainties

Year: 2025

Type: article

Abstract: This correspondence presents a probabilistic generalization of the generalized optimal subpattern assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multiobject estimation applications. The P-GOSPA extends GOSPA into the space of multiBernoulli densities, incorporating inherent uncertainty in probabilistic multiobject representations. In addition, 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.

Source: IEEE Transactions on Aerospace and Electronic Systems

DOI: 10.1109/taes.2025.3580523

Authors: Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson, Kuo-Chu Chang, Ting Yuan, Lennart Svensson

Institutions: Shanghai Jiao Tong University, Universidad Politécnica de Madrid, KTH Royal Institute of Technology, George Mason University, Chalmers University of Technology

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Shanghai Jiao Tong University

OpenAlex ID: W4411799025

FWCI: 0.822

Cites: 21

Cited by: 2

Related to: 10