Improvement of multiple ground targets tracking with fusion of identification attributes

Multiple ground targets (MGT) tracking is a challenging problem in real environment. Advanced algorithms include exogeneous information like road network and terrain topography. In this chapter, we develop a new improved VS-IMM (Variable Structure Interacting Multiple Model) algorithm for GMTI (Ground Moving Target Indicator) and IMINT (IMagery INTelligence) tracking which includes the stop-move target maneuvering model, contextual information (on-off road model, road network constraints), and ID (IDentification) information arising from classifiers coupled with the GMTI sensor. The identification information is integrated to the likelihood of each hypothesis of our SB-MHT (Structured Branching Multiple Hypotheses Tracking). We maintain aside each target track a set of ID hypotheses with their committed beliefs which are updated on real time with classifier decisions through target type tracker based on a proportional conflict redistribution fusion rule developed in DSmT. The advantage of such a new approach is to deal precisely and efficiently with the identification attribute information available as it comes by taking into account its inherent uncertainty/non-specificity and possible high auto-conflict.