Papers
9
Total Citations
173
H-Index
5
About
Mohamed Atia is a prominent researcher specializing in indoor navigation, sensor fusion, and autonomous positioning systems — fields critical to the advancement of robotics, autonomous vehicles, and mobile computing. His most celebrated contribution, the integrated indoor navigation system for ground vehicles (2015, 99 citations), demonstrated a sophisticated fusion of inertial sensors, LiDAR, WiFi signal strength, odometry, and occupancy floor maps to achieve reliable 3D navigation in GPS-denied environments — a landmark achievement in the field. Building on this foundation, Atia has pioneered the application of nonlinear estimation techniques, including particle filters and Extended Kalman Filters, to address the persistent challenges of GPS degradation in urban and indoor settings. His work on optimizing Kalman filter parameters using genetic algorithms and design of experiments reflects a broader commitment to making sensor fusion both practical and computationally efficient. Atia has also explored Bayesian machine learning approaches for inertial/WiFi navigation integration and robotic motion planning. His research culminates in a 2025 book on sensor fusion for positioning and mapping, cementing his role as both a researcher and educator shaping the next generation of navigation technology.
Research Focus
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- 9Sensor Fusion Approaches for Positioning, Navigation, and Mapping1 citations · 2025