Resolution Improvement in OFDM-based Joint Communication and Sensing through Combined Tracking and Interpolation

We investigate a monostatic orthogonal frequency-division multiplexing (OFDM)-based joint communication and sensing (JCAS) system with multiple antennas for object tracking. The native resolution of OFDM sensing, and radar sensing in general, is limited by the observation time and bandwidth. In this work, we improve the resolution through interpolation methods and tracking algorithms. We verify the resolution enhancement by comparing the root mean squared error (RMSE) of the estimated range, velocity and angle and by comparing the mean Euclidean distance between the estimated and true position. We demonstrate how both a Kalman filter for tracking, and interpolation methods using zero-padding and the chirp Z-transform (CZT) improve the estimation error. We discuss the computational complexity of the different methods. We propose the KalmanCZT approach that combines tracking via Kalman filtering and interpolation via the CZT, resulting in a solution with flexible resolution that significantly improves the range RMSE.