Guest Editors:
Tai Fei, PhD, Dortmund University of Applied Sciences, Dortmund, Germany
Shunqiao Sun, PhD, The University of Alabama, Tuscaloosa, United States of America
Markus Gardill, PhD, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
Changzhi Li, PhD, Texas Tech University, United States of America
Rick S. Blum, PhD, Lehigh University, Bethlehem PA, United States of America
Martin Haardt, PhD, Ilmenau University of Technology, Ilmenau, Germany
Sevgi Z. Gurbuz, PhD, North Carolina State University in Raleigh, United States of America
EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on Advances in Signal Processing for Automotive Radar: Robustness, Performance, and Emerging Techniques. Automotive radar plays a crucial role in modern Advanced Driver Assistance Systems (ADAS) and autonomous driving. However, the increasing complexity of real-world driving scenarios poses significant challenges for radar signal processing, including interference mitigation, sensor fusion, and real-time scalability. Emerging signal processing techniques—such as (model-based) deep learning, tensor-based methods, and distributed radar processing—offer promising solutions to enhance the robustness and efficiency of automotive radar systems. Additionally, challenges related to mutual interference, waveform design, and processing in distributed and cross-modal systems require novel algorithmic approaches. This special issue provides a venue for cutting-edge research in automotive radar signal processing, addressing the latest advancements in DoA estimation, array processing, real-time implementation, and AI-based techniques. Contributions focusing on robust, scalable, and real-time solutions for large-scale commercial applications are particularly welcome.
Here’s the link for reference