Sparse Sensor Array Processing for High-Resolution Sensing

Yimin D. Zhang, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering
Temple University, Philadelphia, PA
IEEE Fellow, SPIE Fellow, IEEE SPS Distinguished Lecturer
http://yiminzhang.com

2/2/2024
11am – 12pm CST
Location: San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Organizers and Sponsors:
UTSA MATRIX AI Consortium; UTSA Department of ECE; IEEE Signal Processing Society (Distinguished Lecturer Program); IEEE Lone Star Section, Joint Communications and Signal Processing Chapter

Abstract:
Array signal processing plays a crucial role in various applications, including radar sensing, wireless communications, and medical imaging. It serves as a vital technology for tasks like directional beamforming, interference suppression, multiuser access, direction finding, signal localization, and computational imaging. The evolving needs of modern radar sensing and wireless communications demand higher spatial resolution and improved estimation accuracy. However, these requirements often translate into a significant hardware cost due to the large array aperture and the high number of array sensors.To address these challenges, sparse sensor array design and processing offer low-complexity solutions that enhance sensing capability and improve performance with a reduced number of array sensors. This presentation explores recent advancements in concepts, design strategies, and processing methods for sparse array-based sensing, aiming to achieve high spatial resolution and an increased number of degrees of freedom. These advancements leverage recent progress in compressive sensing, optimization, and machine learning. Various sparse array processing techniques will be discussed and array designs that reduce difference lag redundancies and exploit frequency diversity will be presented, providing insights into efficient and cost-effective solutions for array signal processing in diverse applications.