基于深度学习杂波抑制的多基地 ISAC 框架面向 5G 及未来网络
Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond
AI 导读
一篇 arXiv 论文提出面向 5G 及未来信号的多基地 ISAC（通感一体化）框架，用于高保真估计目标位置与速度。该框架将分布式杂波建模为随机分布、具有不同多普勒扩展和雷达截面积的散射体，并引入残差与注意力增强的深度神经网络直接从受杂波污染的观测中增强目标响应。传统信号处理基线在强杂波下鲁棒性有限，而深度学习方法在多种杂波条件下的目标位置和速度估计上显著更优。
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