面向5G及后5G的多基地ISAC与基于深度学习的杂波抑制
Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond
Yalin E. Sagduyu, Tugba Erpek, Sastry Kompella
AI 速览：该论文提出一种多基地ISAC框架，利用残差与注意力增强的深度神经网络从杂波污染观测中直接增强目标响应，以提升目标位置与速度估计的鲁棒性。
Integrated sensing and communications (ISAC) systems using 5G and beyond signals of opportunity enable joint wireless sensing and communication using shared infrastructure and spectrum resources. While multistatic ISAC can increase sensing coverage with spatially distributed receivers, dense and dynamic clutter can significantly degrade bistatic range and Doppler estimation, leading to errors in target localization and velocity estimation. This paper presents a multistatic ISAC framework with clutter suppression for high-fidelity estimation of target position and velocity. Distributed clutter is modeled with randomly located scatterers with varying Doppler spreads and radar cross sections to capture dense and dynamic clutter effects. A broad range of conventional signal processing baselines are evaluated for clutter suppression, showing limited robustness under strong clutter conditions. To address this limitation, deep learning-based clutter suppression methods are introduced to improve robustness against dense and dynamic clutter by employing residual and attention-enhanced deep neural network architectures that directly enhance target responses from clutter-contaminated observations. These methods substantially outperform conventional approaches in target position and velocity estimation across diverse clutter conditions in multistatic ISAC.