Temporal Graph Neural Network for ISAC Target Detection and Tracking

TL;DR

This paper introduces a novel temporal graph neural network approach for multi-target detection and tracking in ISAC systems, leveraging delay-Doppler information to improve accuracy over traditional methods.

Contribution

The paper presents a TGNN-based method that models delay-Doppler maps as graph sequences for joint clustering and data association, advancing ISAC target tracking capabilities.

Findings

Reduced NMSE in delay and Doppler estimates

More accurate multi-target tracking compared to Kalman filter baseline

Effective across diverse scenes with varying target dynamics

Abstract

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.