VIRTUAL Distinguished Lecture with Kamal Sarabandi: Polarimetric Radar Sensors for Detection of Power Lines in Strong Clutter Background

Date/Time
Date(s) - 29 Oct 2021
1:10 PM - 2:00 PM

Location

Headshot photo of Kamal SarabandiSpeaker: Kamal Sarabandi, Rufus S. Teesdale Endowed Professor of Engineering with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor

Title: Polarimetric Radar Sensors for Detection of Power Lines in Strong Clutter Background

Abstract: High voltage powerlines and their supporting towers are considered a major safety hazard for helicopters and other low-flying aircrafts. A relatively recent study indicated that about 10 wire strike accidents per 100,000 hours of flight occurs in the United States. To prevent such accidents, development of small-size and low-cost millimeter-wave collision avoidance radar systems were considered in my group in early 2000s and such studies were continued up until recent years. In this talk, the radar phenomenology of high voltage power lines and cables for examining the feasibility of detecting power lines along the path of a low-flying aircraft using millimeter-wave radar systems is presented. For this purpose, electromagnetic scattering models and polarimetric backscatter measurements of power line samples of different diameters and strand arrangements will be presented. In addition, the effects of a thin layer of water and a layer of ice over the power line surface on its polarimetric scattering behavior are studied experimentally. Based on this phenomenological study, a polarimetric detection algorithm that makes use of the scattering features caused by the braided structure of power lines is developed. The coherence between the co- and cross-polarized backscatter components is used as the detection parameter. This statistical detection parameter can be applied to any extended target such as a suspended cable in clutter background. It is shown that the proposed algorithm is capable of detecting power lines in a relatively strong clutter background with a poor signal-to-clutter ratio. The performance of the algorithm is demonstrated experimentally using a controlled experiment. In addition, the performance of the algorithm for mapping power lines in SAR images is demonstrated using a number of low-grazing incidence polarimetric SAR images at 35 GHz.

Bio: Kamal Sarabandi is the Rufus S. Teesdale Endowed Professor of Engineering at The University of Michigan. His research areas of interest include microwave and millimeter-wave radar remote sensing, Meta-materials, electromagnetic wave propagation, and antenna miniaturization. Professor Sarabandi has supervised 58 Ph.D. and numerous Masters students and postdoctoral fellows. He has published many book chapters, more than 310 papers in refereed journals, and more than 740 conference papers. He, together with his students, are recipients of 35 paper awards. Dr. Sarabandi served as a member of NASA Advisory Council for two consecutive terms from 2006-2010 and served as the President of the IEEE Geoscience and Remote Sensing Society (2015-2016). He is currently the Chair of Commission F of USNC/URSI and serving as member of the AdCom for the IEEE Antennas and Propagation Society. He led the Center for Microelectronics and Sensors funded by the Army Research Laboratory (2008-2018) and is leading the Center of Excellence in Microwave Sensor Technology. His contributions to the field of electromagnetics have been recognized by many awards including Humboldt Research Award, the IEEE GRSS Distinguished Achievement Award, the IEEE Judith A. Resnik medal, the IEEE GRSS Education Award, NASA Group Achievement Award, and many other wards from the University of Michigan. He is a Fellow of the IEEE, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow of the National Academy of Inventors. He is a member of the National Academy of Engineering.

Seminar Host: Reza Zoughi

Webinar Link: https://iastate.zoom.us/j/94638028031?pwd=Tzh1SVdxRTZmanBwbXIyN3VDNjVXdz09

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