Advances in Array Signal Processing: Methods and Applications

Dear Colleagues,

This Special Issue focuses on advancing the theory, methodology, and practical applications of Array Signal Processing (ASP), aiming to bridge classical techniques, cutting-edge innovations, and deep learning-driven methods while addressing real-world challenges in complex signal environments.

The Issue welcomes original research, reviews, and case studies across applications (communication, radar, sonar, acoustic sensing) to foster cross-disciplinary dialog and accelerate the translation of ASP innovations into practical systems.

The topics of interest include, but are not limited to, the following:

• Deep learning-based ASP: Exploring how neural networks (fully connected deep neural networks (FC-DNNs), convolutional deep neural networks (CNNs), Transformers, graph neural networks) and data-driven frameworks can improve ASP performance—for example, end-to-end DOA estimation without prior signal models, adaptive beamforming for dynamic interference, or array fault diagnosis via anomaly detection. On the other hand, in order to deploy deep learning-based methods on the embedded systems, lightweight versions of the deep learning-based ASP methods are also appreciated.
• Traditional ASP technologies such as beamforming, direction-of-arrival (DOA) estimation (MUSIC, ESPRIT), and array calibration, which remain foundational but require optimization for modern scenarios (e.g., low signal-to-noise ratio, non-stationary interference).
• Advanced ASP methods such as sparse representation methods, sparse array design, and distributed array processing, which overcome classical array limitations (e.g., aperture constraints, spatial aliasing) to enhance spatial resolution and adaptability.
• New applications of ASP: Non-contact respiratory and heartbeat detection using millimeter-wave radar, autonomous driving, and intelligent transportation fields.

We look forward to receiving your contributions.

Dr. Aifei Liu
Prof. Dr. Shengguo Shi
Dr. Feng Chen
Guest Editors

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