Robust FMCW Radar Clutter Suppression for UAV Detection via Learning Physics-Informed Priors

The rapid development of the low-altitude economy has made reliable UAV surveillance in urban and field areas a critical task. For FMCW radar, detecting these low, slow, and small targets is severely compromised by strong clutter, particularly the pervasive sidelobe clutter from static infrastructures that masks faint target echoes. To address this challenge, this paper proposes the Physics-Informed Guided Decomposition Network (PIGD-Net) for robust and adaptive radar clutter suppression. Firstly, we reformulate the clutter suppression task as a guided component decomposition problem. Subsequently, a multi-objective optimization framework is introduced. The framework employs a convolutional sparse-enhancement module to accurately capture the target component, while concurrently suppressing the low rank clutter via a guided nuclear norm minimization, which is dynamically steered by a data-driven mask. This entire optimization process is then unfolded into the PIGD-Net architecture, enabling efficient training by accommodating the distinct characteristics of its internal module. Crucially, PIGD-Net operates under a composite unsupervised loss, enforces the inherent physical properties of each component by fusing a reconstruction term, a Hoyer-based sparsity prior, and an orthogonality penalty, thereby obviating the need for ground-truth labels. Extensive experiments on simulated and real world data validate the performance of the method. Compared to existing methods, PIGD-Net achieves efficient suppression with a fast processing time, while significantly improving the Signal-to Clutter Ratio and target fidelity.