Deep learning-based clutter suppression using residual and attention-enhanced neural networks substantially improves target localization and velocity estimation in multistatic ISAC systems compared to conventional signal processing methods, especially under dense clutter conditions.
This paper tackles clutter suppression in multistatic ISAC systems (which combine wireless sensing and communication using 5G signals) by comparing traditional signal processing methods against deep learning approaches.