UAV Trajectory Monitoring for Integrated Sensing and Communications System

In this paper, we present a framework to enable unmanned aerial vehicle (UAV) trajectory monitoring for an integrated sensing and communications (ISAC) system. Specifically, the base station (BS) first performs beam-scanning to acquire the echo signals from dynamic targets. Static environmental clutter is subsequently filtered out to enable real-time target detection. Next, we propose a phase-rotated discrete Fourier transform (PRDFT) algorithm to estimate the targets’ motion parameters, including distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity. We then convert the estimated parameters into a common Cartesian coordinate system to extract the targets’ positional and velocity features. To associate the targets with their corresponding trajectories, we propose a position wave gate and velocity differences nearest neighbor (WGVDNN) algorithm that matches targets based on similar position and velocity features relative to the trajectories. Afterward, we apply the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to identify the targets’ motion model and predict their positions in the next time slot, thereby directing the beam to track the discovered ones. Simulation results demonstrate that the proposed framework effectively enables the real-time discovery of new targets and the continuous tracking of the discovered targets, thereby monitoring the complete trajectories of all targets.