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Reminder: IEEE NL CCCS Chapter Technical Event with Dr. Khalid El-Darymli

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IEEE NL CCCS Chapter Technical Event - Deep Sigma Point Processes for Radar Cross Section Modeling in Spaceborne SAR Imagery

IEEE Newfoundland-Labrador Computer, Communication, and Circuits & Systems Joint Societies Chapter cordially invites you to a virtual technical presentation entitled “Deep Sigma Point Processes for Radar Cross Section Modeling in Spaceborne SAR Imagery” by Dr. Khalid El-Darymli, Defence Research and Development Canada (DRDC), Ottawa, ON, Canada.

When: 11:00am-12:00pm, Jan. 24, 2025 (Friday)

Where: Virtual. See event location for more info.

Seminar Title: Deep Sigma Point Processes for Radar Cross Section Modeling in Spaceborne SAR Imagery

Seminar Abstract:

Radar Cross Section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This talk introduces a Deep Sigma Point Process (DSPP) model for predicting RCS in Synthetic Aperture Radar (SAR) imagery, using a RADARSAT-2 dataset containing 208,191 verified ships. The DSPP model not only strives for predictive accuracy but ventures to characterize the uncertainty inherent in the intricate relationships among radar signals, ship parameters, and environmental conditions. Unlike traditional approaches relying on deterministic equations with static parameters, the DSPP leverages a hierarchical Gaussian Process framework with Bayesian inference to capture variability and uncertainty in RCS predictions. By generating predictive distributions rather than single estimates, the model effectively accounts for the complex dynamics governing radar returns. Using a Matérn kernel with Automatic Relevance Determination, the DSPP identifies and ranks critical features across radar, operational, and environmental domains, ensuring transparency and interpretability. Performance evaluations demonstrate the model's superiority over linear regression baselines, achieving a 20.83% reduction in Root Mean Squared Error (RMSE), a 25.89% increase in R², and a 44.4% reduction in both residual Interquartile Range (IQR) and Median Absolute Deviation (MAD) on test data. By providing calibrated uncertainty bounds, the DSPP enhances prediction reliability and supports robust decision-making. This work marks a shift toward probabilistic models that incorporate the inherent uncertainty of complex phenomena. Transitioning from fixed equations to distributions over outcomes, the DSPP fosters a deeper understanding of RCS behavior, enabling systems to thrive in dynamic operational environments.

Biography of the Speaker:

Khalid El-Darymli (Senior Member, IEEE) received the B.Sc. degree in electrical engineering from Garyounis University, Benghazi, Libya, in 2001, the M.Sc. degree in computer and information engineering from the International Islamic University of Malaysia, Kuala Lumpur, Malaysia, in 2006, and the Ph.D. degree (with distinction) in electrical engineering from Memorial University, St. John’s, NL, Canada, in 2015.

From 2010 to 2014, he was a Doctoral Researcher with C-CORE, St. John’s, where he developed algorithms for target recognition in SAR imagery. From 2014 to 2017, he worked as a Senior Engineer with Northern Radar Inc., St. John’s, co-developing a high-frequency software-defined radar for coastal ocean applications. Concurrently, he served as an instructor for both graduate and undergraduate courses in antennas with the Department of Electrical Engineering, Faculty of Engineering and Applied Science, Memorial University.

From mid-2017 to early 2021, Dr. El-Darymli was a Research and Development Scientist with MDA Systems, Richmond, BC, Canada, where he contributed to several projects, including laser guide stars for optical communications, ground moving target indication (GMTI), velocity estimation in SAR imagery, and target recognition in inverse SAR (ISAR) imagery. He is a licensed Professional Engineer (P.Eng.) with the Association of Professional Engineers and Geoscientists of Alberta.

Since mid-2021, he has been a Defence Scientist with Defence Research and Development Canada (DRDC), Ottawa, ON, Canada, focusing on various aspects of SAR and ISAR imagery, along with other research and development activities. In June 2023, he was appointed Affiliate Faculty with the Fowler School of Engineering, Chapman University, Orange, CA, USA.

Dr. El-Darymli is a Fellow of the School of Graduate Studies. During his Ph.D. studies, he received the Ocean Industries Student Research Award from the Research and Development Corporation (InnovateNL), Government of Newfoundland and Labrador, for his work on SAR Detection in Cluttered Environments. He also won the IEEE NECEC 2013 Wally Read Best Student Paper Award. During his M.Sc. studies, he developed an award-winning Speech to American Sign Language Interpreter System using machine learning techniques.