The talk will explore the question, Can mm-wave radars perceive objects well outside their field of view - for instance, objects placed fully behind the radars or entirely obstructed by obstacles? Traditional radars are limited to perceiving objects through signals that scatter exactly once from the radar to the object and back to the radar. In practice, however, signals from the radar to a given object may scatter off multiple other intermediate objects (e.g. walls, people, etc.) owing to signal multipath. In traditional radar signal processing, these additional signal bounces are viewed as unwanted clutter that must be eliminated. This talk will present a framework to explicitly model such multi-bounce paths as a tool to observe objects that are occluded to traditional radar methods. Several state-of-the-art methods have exploited multipath for radar sensing. However, they make specific assumptions on the number of bounces, require additional hardware or assume prior knowledge of the environment - requirements that the proposed method avoids. The proposed method was implemented on a commercial mm-Wave radar platform, and through a set of exhaustive experiments the enhancement in field-of-view beyond the system's transmit beam pattern was demonstrated. The possible use cases include autonomous navigation, disaster management, and Joint communications and sensing systems, to name a few.
Divyanshu Pandey received the B.Tech. degree in communication and computer engineering from the LNM Institute of Information Technology, Jaipur, India, in 2011, the M.S. degree in electrical engineering from the University of Minnesota, Twin Cities, USA, in 2014, and the Ph.D. degree in electrical engineering from McGill University, Montreal, QC, Canada, in 2022. Between 2011 and 2013, he worked as an Assistant Manager in the Instrumentation team with HMEL, Bathinda, India. He also worked as a Wireless Systems Engineer with Marvell Semiconductors Inc., Santa Clara, CA, USA, from February 2015 to August 2017. He is a recipient of the Outstanding TA award from the Faculty of Engineering at McGill University and the Best Student Paper award at FICC 2021. He is currently a Postdoctoral Associate with the Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA where he mentors graduate students in their research and teaches a course on Modern Communication Theory. His research interests include wireless communication systems and networks, radar imaging, joint sensing and communication, information theory, and tensor algebra with applications to communications and signal processing.