arXiv cs.AI· Yalin E. Sagduyu, Tugba Erpek, Sastry Kompella·· 14 小时前AI 评分18
面向5G及未来网络的多基地ISAC：基于深度学习的杂波抑制
Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond
AI 导读
该论文提出一种带杂波抑制的多基地ISAC框架，用于高保真估计目标位置与速度。研究用随机分布、多普勒扩展和雷达截面积各异的散射体建模分布式杂波，并评估多种传统信号处理基线，发现其在强杂波下鲁棒性有限。为此引入基于残差和注意力增强深度神经网络的深度学习杂波抑制方法，在多基地ISAC多种杂波条件下，其位置与速度估计显著优于传统方法。
正文 · 原文
Abstract: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.
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.40586 [cs.NI] |
| (or arXiv:2610.40586v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40586 arXiv-issued DOI via DataCite |
Submission history
From: Yalin E. Sagduyu [view email]
[v1]
Thu, 29 Oct 2026 17:47:02 UTC (362 KB)
来源：arXiv cs.AI · arxiv.org