Sparsity-Based Multipath Exploitation for Through-the-Wall Radar Imaging

Description

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. Front Matter ....Pages i-xx Introduction and Motivation (Michael Leigsnering)....Pages 1-8 Fundamentals of Compressive Sensing (Michael Leigsnering)....Pages 9-19 Signal Model (Michael Leigsnering)....Pages 21-37 Sparsity-Based Multipath Exploitation (Michael Leigsnering)....Pages 39-76 Mitigating Wall Effects and Uncertainties (Michael Leigsnering)....Pages 77-97 Conclusions and Outlook (Michael Leigsnering)....Pages 99-103 Back Matter ....Pages 105-108