Ransara Wijitharathna
Papers
1
Total Citations
2
H-Index
1
About
Ransara Wijitharathna is an emerging researcher at the forefront of autonomous robotics and advanced radar signal processing. Her work centers on integrating graph neural networks (GNNs) with millimeter-wave radar systems to overcome critical limitations in autonomous navigation. In her most-cited paper, "Graph Neural Network Based 77 GHz MIMO Radar Array Processor for Autonomous Robotics," she addresses the fundamental trade-off between range and beam scanning time in FMCW MIMO radars. By proposing a novel GNN-based approach to process radar array data, Wijitharathna demonstrates how machine learning can enhance radar performance without sacrificing real-time responsiveness—a key requirement for autonomous systems. Her contributions sit at the intersection of deep learning, sensor fusion, and robotics, offering a pathway to more reliable perception in challenging environments. Though early in her career, with her work already garnering citations, she is establishing herself as a promising voice in next-generation radar processing. Her research holds particular significance for autonomous vehicles and field robotics, where robust long-range sensing is essential for safe operation.
Research Focus
Key Achievements
Top Papers
- 1