Robust DOA Estimation Based on Subarray Learning Selection

We propose a learning-based DOA framework that selects subarray elements dynamically to address array distortion and path-dependent loss. A lightweight classifier is trained to activate subarrays based on spatial features, and a physicsinformed loss improves generalization. Simulations using Bellhop show accuracy gains over classical baselines in low SNR and distortion settings.