Signal Processing for Distributed RF Sensing in Complex Environments

ABSTRACT

Distributed radio frequency (RF) sensing systems, including multiple-input multiple-output (MIMO) radar and radar architectures assisted by reconfigurable intelligent surfaces (RIS), face significant challenges in complex environments. Target detection in non-line-of-sight (NLOS) scenarios, where direct propagation paths are blocked, is a key motivation for these advanced architectures. In complex environments, issues such as synchronization errors, non-orthogonal waveforms, multipath propagation, interference and environmental clutter further complicate robust detection. This talk discusses signal processing methods for robust target detection and interference suppression across a range of distributed sensing architectures. We examine how non-orthogonal waveforms and asynchronous transmission impact distributed MIMO radar. For RIS-assisted radar, we also consider the joint design of active and passive beamforming, enabling flexible sensing strategies and enhanced detection capabilities. Key technical challenges, including optimization and implementation complexity, are discussed. Together, these approaches highlight the potential for distributed sensing systems, incorporating both MIMO and RIS resources, to achieve reliable performance in realistic and challenging scenarios.

BIOGRAPHY

Dr. Cengcang Zeng is currently a postdoctoral fellow in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. He received his Ph.D. and M.S. degrees in Electrical Engineering from Stevens Institute of Technology in 2024 and 2018, respectively, and his B.S. degree from Nanjing University of Aeronautics and Astronautics in 2016. His research focuses on statistical signal processing, convex optimization, and robust detection for radar and RF sensing systems, with an emphasis on joint beamforming design and intelligent environment control in complex scenarios.