Evidential Data Association: Benchmark of Belief Assignment Models
Mohammed Boumediene · 2019 International Conference on Advanced Electrical Engineering (ICAEE) · 2019
Data association is an important task in Multiple Target Tracking (MTT) systems. The purpose is to assign the sensor detections to the known objects. However, sensor data can be inaccurate and incomplete. The Evidential theory provides interesting tools to manage ignorance and data imperfection. In fact, the Evidential theory is considered as a generalized form of the Bayesian theory which enables reasoning with uncertainty. The Evidential data association quantifies sensor information by belief masses. Subsequently, the combination of these masses provides more accurate data to make-decision on associations. Most published studies focus on decision-making algorithms. In this paper, the importance is done to belief masses estimation which is a crucial step in data fusion. There are two estimation models: antagonist and non-antagonist. The objective of this paper is to benchmark both models on real-data. The obtained results show that the non-antagonist mass function is more suited for data association problems.