Chairs: Assoc. Prof. Cai Wen (Northwest University), Assoc. Prof. Yan Huang (Southeast University), Assoc. Prof. Zhanye Chen (Chongqing University)

Abstract:

Optimization have been playing an essential role in modern radar systems for decades to achieve high accuracy and robustness. With the rapid development of machine learning (ML), radar researchers have started seeking for data-driven methods to tackle the increasing complex electromagnetic environment. In the latest ten years, ML methods have sparked a large amount of research across multiple disciplines including signal processing, data mining and communications. Key techniques such as matrix/tensor factorization, convolutional neural networks and reinforcement learning, etc., have been advancing many classical and timely radar signal processing problems, e.g., target detection and localization, beamforming and radar imaging – to name just a few. This special session aims to bring together researchers and experts from the broader signal processing, data mining, and machine learning communities to address existing and arising challenges in radar signal processing.

The session aims at bringing together top experts from the radar signal processing community using machine learning and optimization as a ‘bridge’. Communicating with experts across different disciplines can oftentimes spark new ideas, discovering new applications, and inspire exciting future work. The organizers have a unique position for putting together such a program: They have been working on the disciplines of interest for many years, and they are well-connected in the field of machine learning and optimization methods. The invited contributors are also leading experts or rising stars. This makes cross-disciplinary collaboration likely to happen. Exciting interactions and collaborations are expected to be nurtured during the proposed event.