Mahshid Asri’s Proposal Review

“Development of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications”

Abstract:
Detection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security.
This work proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body, we extract the nominal body contour using Fourier series, separate body and object responses, categorize the object as lossy or lossless based on the depression and protrusion of the body contour, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good, implying fewer nuisance alarms at check points. The second project is a metal detection algorithm designed to monitor pedestrians walking along a sidewalk for large, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system.

Committee:

Prof. Carey Rappaport (Advisor)

Prof. Charles DiMarzio

Prof. Edwin Marengo