Reconfigurable Intelligent Surface Assisted Mobile Target Tracking With Agile Phase Control

As a typical demand of the next-generation wireless networks, numerous emerging applications impose stringent requirements on the timely and accurate sensing of the dynamical channel and the moving target. And radar with massive arrays could satisfy these requirements, imposing large power consumption and complexity. To this end, thanks to the energy-efficient and cost-effective characteristics, reconfigurable intelligent surface (RIS) has been considered as one of the most promising candidates for assisting environment sensing owing to its ability of manipulating the impinging signal. Inspired by this, we investigate a novel application of RIS for target tracking in high-mobility and three-dimensional scenario. Specifically, we consider a RIS-assisted integrated sensing and communication (ISAC) system where a RIS is deployed at the base station (BS) side. Exploiting the proposed predictive phase design strategy and collected echoes, the RIS tries to estimate the motion parameters of the potential targets. In particular, to fulfill the goal of precise and timely target tracking, we adapt a particle filtering (PF) based and an extended Kalman filtering (EKF) based state estimation algorithms, where both the observations at RIS side and the inherent state evolution information are well exploited. Furthermore, numerical results are provided to validate the effectiveness of the proposed predictive phase design strategy in various scenarios. The performance of EKF scheme is superior to the PF about 15% averagely with lower computational complexity.