Multimodal data association based on the use of belief functions for multiple target tracking

In this paper we propose a method for solving the data association problem within the framework of multi-target tracking, given a set of environmental measurements obtained by complementary and redundant sensors. The proposed method exploits belief theory, which is a powerful tool for handling imperfect data. We applied the method to situations where colored moving targets emit an audio signal. The basic belief assignment is computed using a confidence measure between targets and incoming measurements based on multi-modal attributes. This allows the ambiguity in association between measurements and targets to be reduced especially for targets that come closely spaced. The proposed method has been tested using different sets of simulated data. The results obtained are very satisfactory and show that the method provides a useful mechanism for data association.