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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08306 |
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| _version_ | 1866913018737065984 |
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| author | Sanaie, Saiedeh Maboud Grossmann, Marcus Landmann, Markus Dallmann, Thomas |
| author_facet | Sanaie, Saiedeh Maboud Grossmann, Marcus Landmann, Markus Dallmann, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08306 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Temporal Graph Neural Network for ISAC Target Detection and Tracking Sanaie, Saiedeh Maboud Grossmann, Marcus Landmann, Markus Dallmann, Thomas Signal Processing 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. |
| title | Temporal Graph Neural Network for ISAC Target Detection and Tracking |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.08306 |