Dr. Fauzia Ahmad Secures CAC’s First ARO Research Award
The Army Research Office (ARO) has awarded Dr. Fauzia Ahmad, Research Associate Professor in the Center for Advanced Communications (CAC) and Director of the Radar Imaging Laboratory, a three-year, $448,000 research grant to leverage emerging compressive sensing (CS) techniques to achieve efficient and enhanced radar imaging for urban sensing applications. This grant represents the first research award from the ARO secured by the CAC’s professors since the Center’s inception more than 20 years ago.
"I am so pleased that past and recent work we have done in collaboration with the Army Research Lab to advance through-the-wall imaging bore fruit and serves as a conduit for our successful ARO proposal. The CS research for indoor imaging conducted within the CAC can play a critical role in helping the U.S. Army achieve its objective of providing persistence surveillance in urban environments in a fast and reliable manner," says Dr. Ahmad.
Through this research project, entitled "Multipath Exploitation and Knowledge-Based Urban Radar," Dr. Ahmad and co-principal investigator Dr. Moeness Amin, Director of the CAC, will utilize advances in CS for improved target detection, localization, and tracking in urban canyons and enclosed structures. "Our goal is to achieve significant reductions in data volume and acquisition time without compromising system performance," says Dr. Ahmad.
The goal of achieving quick turnaround and actionable intelligence is primarily challenged due to the use of large signal bandwidth and array apertures to capture high resolution images, which can be costly and time-consuming. CS relies on the fact that typical imaged scenes and events are often sparse in nature with large empty or blank backgrounds. This is the case whether optical, ultrasound, or radar sensing modality is used. With this assumption in place, CS provides means for improvements and efficiencies in information extraction and data acquisition time. CAC researchers will focus primarily on the development of models and algorithms for effective utilization of both prior information about the scene and exploitation of the rich multipath indoor environment.