Exploiting formal bayesian framework for data fusion in multi-sensor systems

We consider a multi-target tracking problem that aims to simultaneously determine the number and state of mobile targets in the field. Conventional paradigms tend to report only the existence and state of targets according to centralized detection and data fusion. On the contrary, we investigate a multi-target, multi-sensor scenario in which (a) both the number and the state of the targets are unknown a priori; and (b) the detection with respect to targets is performed in a compressive manner. Toward this end, we exploit random finite set theory (RFST), a statistical tool based on Bayesian framework, for establishing generalized likelihood and Markov density functions to yield an iterative filtering procedure. The nature of RFST allows us to model the uncertainty of missed detection, target disappearance and other practical artifacts. We conduct a study regarding how the design of compressive detection has impact on the result of system level information fusion.