Probabilistic GOSPA: A metric for performance evaluation of multi-object filters with uncertainties

Artikel i vetenskaplig tidskrift, 2025

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.