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This thesis reports on sparsity-based multipath exploitation methods for through-the-wall radar imaging. Multipath creates ambiguities in the measurements provoking unwanted ghost targets in the image. This book describes sparse reconstruction methods that are not only suppressing the ghost targets, but using multipath to one's advantage. With adopting the compressive sensing principle, fewer measurements are required for image reconstruction as compared to conventional techniques. The book describes the development of a comprehensive signal model and some associated reconstruction methods that can deal with many relevant scenarios, such as clutter from building structures, secondary reflections from interior walls, as well as stationary and moving targets, in urban radar imaging. The described methods are evaluated here using simulated as well as measured data from semi-controlled laboratory experiments.
List of contents
Introduction and Motivation.- Fundamentals of Compressive Sensing.- Signal Model.- Sparsity-Based Multipath Exploitation.- Mitigating Wall Eects and Uncertainties.- Conclusions and Outlook.
Summary
Nominated as an outstanding PhD thesis by the Technische Universität Darmstadt, Germany
Combines the fields of through-the-wall radar imaging and compressive sensing
Demonstrates how image quality can be improved by exploiting multipath and sparse reconstruction techniques
Reports on methods validated for both simulated and measured data
Nominated as “Best Dissertation 2015 in Electrical Engineering and Information Technology” by Vereinigung von Freunden der Technischen Universität zu Darmstadt e.V
