Rashidah Arsat

Papers

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About

Dr. Rashidah Arsat is a leading researcher in wireless communication and indoor positioning systems, with a particular focus on ultra-wideband (UWB) technology. Her most-cited work, "Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network" (2024), addresses a critical challenge in indoor localization: the accurate distinction between line-of-sight (LOS) and non-line-of-sight (NLOS) propagation channels. By leveraging deep neural networks, Dr. Arsat has developed a multiclass identification method that significantly enhances distance measurement precision in complex, dynamic environments—such as those encountered in autonomous vehicle navigation. This contribution is vital for improving the reliability of UWB-based positioning in real-world applications, where signal obstructions and multipath effects often degrade performance. With 4 citations to date, her work is gaining traction among researchers in wireless sensor networks and robotics. Dr. Arsat's research bridges the gap between theoretical signal processing and practical deployment, offering robust solutions for high-accuracy indoor positioning. Her innovative approach underscores her commitment to advancing localization technologies that are essential for smart environments and autonomous systems.