Sensor Fusion and Extended Multi-Target Tracking in Joint Sensing and Communication Networks

In this paper, we consider a joint sensing and communication (JSC) network in which multiple base stations (BSs) cooperate through a fusion center (FC) to detect and track the objects present in a supervised area. Every BS acts as a monostatic sensor capable of scanning the environment and sensing the targets while simultaneously communicating with user equipments (UEs). In particular, each BS generates range-angle maps, which are shared with a FC for data fusion and tracking via particle filter (PF) and multi-hypothesis tracker (MHT) algorithms. The performance of the proposed solutions is evaluated by varying the fraction of power and time devoted to sensing to manage the network overhead and offer a sensing/communication trade-off. Numerical results show that the proposed algorithms can successfully track multiple targets with different sizes and behavior in a vehicular scenario, ensuring, e.g., a root mean squared error (RMSE) of the estimated position of a pedestrian less than 50 cm when considering three BSs.