Robot on the Move: Predictive Beamforming for Enhanced Estimation Accuracy in IIoT

In this paper, we consider a practical integrated sensing and communication (ISAC) scenario in industrial Internet of Things (IIoT). In this scenario, a robot acted as a mobile base station (BS) performing sensing to locate a logistics transport robot (LTR) while also communicating with multiple production lines (PL). We predict the motion parameters of LTR in each time slot and derive the Cramér-Rao bound (CRB) of angle and distance estimation. Afterward, we formulate a joint CRB minimization problem by optimizing the transmit beamforming for communication and sensing. We convert the formulated problem into a two-tier alternating optimization approach by constructing precise surrogates for the non-convex objective functions and constraints. A closed-form expression is derived for solving the outer layer problem. In addition, we employ a convex framework to address the inner layer problem. Numerical results validate the superiority of the proposed algorithm, especially when the BS is far away from the sensing target.