Development of anomaly detection and characterization algorithms using wideband radar image processing for security applications

Permanent URL: http://hdl.handle.net/2047/D20621562
Title:
Development of anomaly detection and characterization algorithms using wideband radar image processing for security applications
Creator:
Asri, Mahshid (Author)
Contributor:
Rappaport, Carey M. (Thesis advisor)
Dimarzio, Charles A. (Committee member)
Marengo, Edwin A. (Committee member)
Language:
English
Publisher:
Boston, Massachusetts : Northeastern University, 2023
Date Accepted:
November 2023
Date Awarded:
December 2023
Type of resource:
Text
Genre:
Dissertations
Format:
electronic
Digital origin:
born digital
Abstract/Description:
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 dissertation 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. We have also trained a deep learning model for pixel-wise localization of body worn anomalies. The second project is a metal detection algorithm developed 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. A deep learning model has then been used to predict a pixel level mask for the body and anomaly based on the inputted radar image. Results indicate that the trained U-Net model is capable of detecting anomalies accurately in various scenarios in real time. F1-score of 86% was achieved on the testing data set.--Author's abstract
Subjects and keywords:
Electrical engineering
Electromagnetics
DOI:
https://doi.org/10.17760/D20621562
Permanent URL:
http://hdl.handle.net/2047/D20621562
Use and reproduction:
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