Deep learning improves clutter suppression in 5G multistatic sensing
Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond
Networking and Internet ArchitectureArtificial IntelligenceMachine Learning
Summary
When multiple sensors use 5G signals to locate moving objects, unwanted echoes called clutter can make it hard to tell where things really are and how fast they move. The authors studied usual clutter-removal methods and found they struggle in busy environments with lots of interference. They introduced deep learning techniques that better separate real targets from clutter, improving the accuracy of position and speed estimates. This approach works well even when the clutter is dense and changes quickly.
What this means in practice
- •For telecom infrastructure teams: Enhance 5G-based sensing systems to accurately track objects in environments with heavy radio interference using improved clutter removal.
- •For autonomous vehicle developers: Improve vehicle localization and velocity estimation when relying on communication signals for sensing in cluttered, dynamic urban environments.
Authors
Yalin E. Sagduyu, Tugba Erpek, Sastry Kompella
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.