New data fusion and tracking approaches in multiple targets/distributed sensors network contexts

The problem of target tracking from distributed sensors in a cluttered environment is addressed. The author and P. Forster have introduced a new and original approach in target tracking and target motion analysis. Two variants of this method have been developed, one using the Bayesian probabilities theory, and one using the theory of evidence. These variants and their specificities with regard to classic tracking methods are recalled, with some extensions to the previous work. Special attention is given to the comparison of these two variants, and to difficult cases such as multiple maneuvering targets or crossing targets.<>