A Robust Credal Assignment Solution Based on the Generalized Bayes’ Theorem

Abstract

This paper proposes a new assignment solution based on the Generalized Bayes’ Theorem (GBT) which aims to establish the best matching between two sets of uncertain data. In order to estimate the effectiveness of the proposition, it is compared to the best credal assignment solutions and the well known Global Nearest Neighbor (GNN) algorithm, through synthetic data and a literature example of multi-target tracking scenarios. Given the same input data, the proposed solution gives better assignment results, especially when sensor imprecision increases. However, the proposed solution stills actually computationally more complex than the GNN and the solution proposed by Denoeux et al.