The Joint Communications & Computer Chapter of IEEE Kingston Section is proud to present the following IEEE Lecture:

APPLICATION OF COMPRESSED SENSING THEORY TO RADAR SIGNAL PROCESSING: TUTORIAL AND RECENT DEVELOPMENTS

Date:  Thursday July 11^th, 2019.

Time:  10:30 – 11:30 AM

Location: Royal Military College of Canada, Kingston, Room S4214

Speaker:  Dr. Soheil Salari

Abstract

During the last decade, the emerging technique of compressed sensing has become a popular subject in signal processing and sensor systems since it can reduce the sampling rate and computational complexity of practical systems without performance loss. The technique of compressed sensing has been successfully applied in signal acquisition, image compression, and data reduction. Based on compressed sensing theory, the original radar echo can be sampled at a lower rate, and then the detection and imaging can be implemented. Although the theory of compressed sensing has been investigated for some radar and localization problems, several important questions have not been answered yet. This presentation introduces the main principle of compressed sensing theory, and then reviews some recent developments in the application of the compressed sensing theory to radar signal processing.

Speaker Bio:

Soheil Salari received all degrees in electrical engineering: Ph.D. from K.N. Toosi University of Technology in 2007, M.Sc. from K.N. Toosi University of Technology in 2001, and B.Sc. From University of Kerman in 1998. He held various research/teaching/engineering positions in Iran until 2011. Since 2011, he has served several research appointments with University of Ontario Institute of Technology (UOIT), University of Toronto, Queen’s University, and RMCC. He also collaborated in several industrial projects. Currently, he is working for the government of Canada as a research scientist. His role has been to carry out research, develop new capabilities, and provide technical advice on topics of artificial intelligence, target tracking, and data fusion. His research interests are in the areas of wireless communications, radar and localization, compressed sensing, digital signal processing, machine learning and artificial intelligence, and optimization theory.

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