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Fast 4D Imaging System

February 27, 2017

MIE/ECE Assistant Professor Jose Martinez-Lorenzo awarded $500K NSF CAREER Award for “4D mm-Wave Compressive Sensing and Imaging at One Thousand Volumetric Frames per Second”.

Abstract Source: NSF

Millimeter-wave sensing and imaging systems are used ubiquitously for a wide range of applications, such as atmospheric sounding of the earth to forecast the weather, security monitoring to detect potential threats at airport checkpoints, and biological imaging of superficial tissues for wound diagnosis and healing. These systems typically operate well when the scene dynamics do not change rapidly. Unfortunately this is not the case in emerging societally-important applications like swarms of drones in rescue missions, smart self-driving cars on roadways, or cyber-physical systems searching for suicide bombers when they are on the move. This project will benefit our society with the development of the first four-dimensional (4D) millimeter-wave imaging system operating in fast changing scenarios, in which safety-critical decisions must be made quickly. One of the new applications of this system will be finding security threats, concealed under clothing or inside backpacks, in open areas like shopping malls, sport venues, and office buildings. Specifically, the system will have the capability to scan multiple people moving within a volumetric region of 26 cubic meters, producing 1000 image frames per second in three dimensions, thus outperforming existing millimeter-wave sensing and imaging systems that are currently used at airport checkpoints. In addition to the societal impact, the Principal Investigator (PI) will build a strong educational program through which diverse audiences can understand the principles and limitations of wave-based imaging systems. The integration of research and education will be accomplished through the development of new curricula and research training methods for students, as well as through the elaboration of a roadmap for transitioning students into industry, in collaboration with Northeastern University (NEU) Cooperative Education Program. The outreach plan includes enabling research experiences for K-12, undergraduate, and underrepresented students in collaboration with the Science, Technology, Engineering, and Mathematics (STEM) centers at NEU, as well as education through online materials and public venues.

The research goal of this CAREER program is to understand the theoretical principles and fundamental limitations of adaptable compressive sensing and imaging systems using 4D (temporal and spatial) coding and to develop and experimentally validate these principles through a novel 4D millimeter-wave adaptive compressive imaging radar system. This system will produce 4D volumetric frame rates beyond 1000 frames per second, each frame having over one million pixels. The primary challenges of implementing 4D adaptable imaging systems are the following: 1) the system must be capable of handling variable dynamics, i.e., objects moving with different velocities and located at different focal ranges; 2) the system must sample data with sufficient signal to noise ratio during the limited period of time; and 3) the system must sample data extremely fast to perform fast 4D video reconstruction with high volumetric frame rates. This project will address these challenges as follows: (i) it will develop a new theory that brings together functional analysis, information theory, compressive sensing, and adaptable metamaterials to enhance the information transfer efficiency of sensing systems; (ii) it will develop a new mathematical framework to optimize 4D codes based on the desired information rate and energy efficiency of the compressive imaging system; and (iii) the system will utilize spatial light modulators, vortex-meta-lenses, and compressive reflectors to perform the coding and to dynamically adapt to the state of the imaging region. The result of this research will establish the scientific basis for the proposed new sensing and imaging systems, by enhancing the imaging performance, reliability, and efficiency while reducing the hardware complexity, overall cost, and energy consumption of the system.