Radar Imaging of Airborne Targets : A Primer for Applied Mathematicians and Physicists

Enabling an understanding of the subject and its current mathematical research issues, Radar Imaging of Airborne Targets: A Primer for Applied Mathematicians and Physicists presents the issues and techniques associated with radar imaging from a mathematical point of view rather than from an instrumentation perspective. The book concentrates on scattering issues, the inverse scattering problem, and the approximations that are usually made by practical algorithm developers. The author also explains the consequences of these approximations to the resultant radar image and its interpretation, and examines methods for reducing model-based error.

Radar-based imaging of aircraft targets is a topic that continues to attract a lot of attention, particularly since these imaging methods have been recognized to be the foundation of any successful all-weather non-cooperative target identification technique. Traditional books in this area look at the topic from a radar engineering point of view. Consequently, the basic issues associated with model error and image interpretation are usually not addressed in any substantive fashion. Moreover, applied mathematicians frequently find it difficult to read the radar engineering literature because it is jargon-laden and device specific, meaning that the skills most applicable to the problem''s solution are rarely applied.

Enabling an understanding of the subject and its current mathematical research issues, Radar Imaging of Airborne Targets: A Primer for Applied Mathematicians and Physicists presents the issues and techniques associated with radar imaging from a mathematical point of view rather than from an instrumentation perspective. The book concentrates on scattering issues, the inverse scattering problem, and the approximations that are usually made by practical algorithm developers. The author also explains the consequences of these approximations to the resultant radar image and its interpretation, and examines methods for reducing model-based error.