My research focuses on multitarget tracking, multisensor fusion and resource management for air, space, ground, maritime, underwater and autonomous systems. It covers a diverse range of sensors, including radar, lidar, sonar and video. The primary domains of application are surveillance systems, intelligent transportation and autonomous vehicles. Some of my current areas of interest are: autonomous vehicle tracking and classification, quantum sensing and tracking, satellite scheduling, and tighter performance bounds.
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Ratnasingham Tharmarasa received the B.Sc.Eng. degree in electronic and telecommunication engineering from University of Moratuwa, Sri Lanka in 2001, and the M.A.Sc and Ph.D. degrees in electrical engineering from McMaster University, Canada in 2003 and 2007, respectively. Currently, he is an assistant professor in the Electrical and Computer Engineering Department at McMaster University, Canada. In 2008, he worked as a researcher at DRS Technologies Canada Limited, Ottawa, Canada. From 2008 to 2019, he was a research associate in ECE department at McMaster University, Canada. He is currently an Associate Editor for Elsevier Signal Processing. He was a Guest Editor of the Sensors 2021-2023 Special Issue on Advances in Angle-Only Filtering and Tracking in Two and Three Dimensions.
His main areas of expertise are target tracking, information fusion, sensor resource management and performance prediction with application to surveillance systems, autonomous vehicles and intelligent transportation. He has published more than 60 peer-reviewed journal articles and 65 conference papers in the above areas, in addition to three book chapters. He has also worked on the development of many real-world target tracking, sensor fusion and resource management systems.
B.Sc.Eng. University of Moratuwa, Sri Lanka
M.A.Sc. McMaster University, Canada
Ph.D McMaster University, Canada
Selected:
J. Li, R. Tharmarasa, D. Brown, T. Kirubarajan and K. R. Pattipati, “A Novel Convex Dual Approach to Three-Dimensional Assignment Problem: Theoretical Analysis,” Computational Optimization and Applications, vol. 74, pp. 481-516, May 2019.
R. Tharmarasa, T. Kirubarajan, M. L. Hernandez and A. Sinha, “PCRLB-Based Multisensor Array Management for Multitarget Tracking,” IEEE Transactions on Aerospace and Electronic Systems, vol. 43, no.2, pp. 539-555, April 2007.
Recent:
G. Li, D. Song, W. Bai, K. Han and R. Tharmarasa, “Consensus and Complementary Regularized Non-negative Matrix Factorization for Multi-View Image Clustering,” Information Sciences, vol. 523, pp. 524-538, April 2023.
B. Liu, R. Tharmarasa, M. Florea, R. Jassemi and T. Kirubarajan, “Diver Tracking in Unknown Structured Clutter Background using a Force-based GM-PHD Filter,” Signal Processing, vol. 200, November 2022.
A. Chatterjee and R. Tharmarasa, “Reward Factor-Based Multiple Agile Satellites Scheduling with Energy and Memory Constraints,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no.4, pp. 3090-3103, August 2022.
D. Song, R. Tharmarasa, K. Han, W. Wang, M. McDonald and T. Kirubarajan, “A Bayesian Method for ViSAR Image Fusion Using Effective Reflection Coefficient,” IEEE Sensors Journal, vol. 22, no.10, pp. 9743-9753, May 2022.
A. Chatterjee and R. Tharmarasa, “Effects of Measurement Uncertainties on Multitarget Tracking,” IEEE Instrumentation & Measurement Magazine, vol. 25, no.1, pp. 37-45, February 2022.
D. Mitra, A. Balachandran and R. Tharmarasa, “Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases,” Sensors, vol. 22, no.0.43681, January 2022.
3 unit(s) T. Kirubarajan This course will introduce the advanced concepts and algorithms for multisensor-multitarget tracking under realistic conditions (with imperfect sensors and measurement uncertainties). In addition, this course will deal with multisource information fusion with applications to communications, signal processing and target tracking.
3 unit(s) Staff Classical filter theory; DFT and FFT; FIR and IIR digital filters; effects of finite precision; implementation of DSP-based systems; adaptive filtering; signal compression.
Instructor
Dr. Ratnasingham (Thamas) Tharmarasa
Discrete time signals and systems including z-transform, DFT and FFT; Classical filter theory, FIR and IIR digital filters; effects of finite precision; implementation of DSP systems; adaptive filtering; spectral analysis, signal compression. Three lectures, one tutorial, one lab every other week; first term Prerequisite(s): ELECENG 3TP4 or ELECENG 3TP3
