Integrated sensing and communication (ISAC) is a key enabler of 6G, supporting environment-aware services. A fundamental sensing task in this setting is reliable multi-target detection and tracking. This paper proposes a temporal graph neural network (TGNN)-based tracking method that exploits delay and Doppler information from the wireless channel. The delay-Doppler map is modeled as a sequence of graphs, and tracking is formulated as a temporal node classification problem, enabling joint clustering and data association of dynamic targets. Using ray-tracing-based channel outputs as ground truth, the method is evaluated across multiple scenes with varying target positions, velocities, and trajectories and is compared with a Kalman filter baseline. Results demonstrate reduced normalized mean squared error (NMSE) in delay and Doppler, leading to more accurate multi-target tracking.
@article{arxiv.2604.08306,
title = {Temporal Graph Neural Network for ISAC Target Detection and Tracking},
author = {Saiedeh Maboud Sanaie and Marcus Grossmann and Markus Landmann and Thomas Dallmann},
journal= {arXiv preprint arXiv:2604.08306},
year = {2026}
}