Radar offers some unique capabilities compared to other sensing phenomenologies. For example, radar can operate at long ranges, during the day and night, and in most weather conditions. Synthetic aperture radar (SAR) enables formation of 2D and 3D images of ground scenes for a wide array of military and commercial applications. In this talk, Dr. Linda Moore will discuss current challenges in SAR signal processing, including the challenge of applying machine/deep learning techniques to SAR automatic target recognition (ATR). Measured and synthetic SAR data has been made publicly available by the U.S. Air Force Research Laboratory and can assist in developing new techniques for today’s SAR signal processing challenges. Available data sets will be associated with relevant technical challenges and examples of related IEEE published work will be highlighted.
Linda J. Moore (SM) received a B.S. in computer engineering (2000-2004) from Wright State University (Dayton, Ohio, USA) and an M.S. in electrical engineering (2004-2006) from The Ohio State University (Columbus, Ohio, USA). She received a Ph.D. in electrical engineering (2006-2016) from the University of Dayton (Dayton, Ohio, USA) where she focused on the impact of phase information on radar automatic target recognition.
Dr. Moore is an IEEE Senior Member (2020), served as a Technical Session Chair at the IEEE Radar Conference, Radar Imaging Systems Session (2014) and the SPIE Defense and Commercial Sensing Conference, Algorithms for SAR Imagery Session (2014, 2017).
Dr. Moore has 19 technical publications including journal articles in IEEE Transactions on Aerospace and Electronic Systems (2018), and IEEE Aerospace and Electronics Systems Magazine (2014). She also contributed content to Part VII: Imaging Radar in Stimson’s Introduction to Airborne Radar book (2014) (acknowledgement to AFRL Gotcha Radar Program).
Dr. Moore has focused on innovative solutions for real-time radar processing to create 24/7, all-weather, day/night sensing capabilities. Dr. Moore has strengthened the workforce through internships, technical/strategic guidance, development of “soft skills” (e.g., communication), promotion of professionalism, and emphasis on participation in world-class technical societies like IEEE. Her exemplary science, technology, engineering and mathematics (STEM) leadership and mentoring was recognized in 2020 when she received the IEEE Dayton Section Women in Engineering (WIE) Award. Dr. Moore has significantly contributed to the engineering community by publishing data sets and challenging problems to reduce the barrier of entry for radar signal processing researchers.
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Meeting ID: 895 6244 1115
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