GSERB: A Novel Attention-Integrated Ghost Residual Block for Enhanced DoA Estimation

This paper presents a lightweight neural architecture for efficient direction-of-arrival (DoA) estimation, built upon a novel Ghost Squeeze-and-Excitation Residual Block (GSERB). The proposed block integrates Squeeze-and-Excitation (SE) attention directly into the Ghost feature generation process, enabling selective channel enhancement while minimizing redundant feature computation. Unlike conventional attentionaugmented convolutional networks, the GSERB amplifies informative ghost feature channels prior to fusion with intrinsic representations, thereby improving feature discrimination and representational efficiency. Experimental results showcase that this integration enhances the model's ability to capture spa-tial-spectral dependencies while maintaining a compact design. The resulting network, named GSERB-DoANet, achieves an effective balance between accuracy, scalability, and computational efficiency for real-time multi-source DoA estimation in wireless sensing applications.