About Mohammed Hussain AlSharif Mohammed Hussain AlSharif Postdoctoral Research Fellow, California Institute of Technology (Caltech) Hand Gestures Recognition Ultrasonic-based Localization indoor localization systems underwater acoustic localization communication signal design machine learning Dr. Mohammed AlSharif is a Postdoctoral Fellow at the California Institute of Technology (Caltech) and a recipient of the KAUST Ibn Rushd Fellowship. His research bridges Riemannian geometry, statistical estimation, and learning for autonomous systems, with applications to high-accuracy pose estimation and robotics. Events Presented Events Sep 14 - Sep 20, 2025 Geometric Sensor Fusion for Pose Estimation on Riemannian Manifolds Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech) Sep 18, 11:00 - 12:00 B1 L3 R3119 This seminar introduces a novel geometric sensor fusion framework that integrates inertial measurements with acoustic ranging on Riemannian manifolds to achieve robust, high-accuracy pose estimation for autonomous systems in GPS-denied environments. Dec 12 - Dec 18, 2021 Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech) Dec 16, 14:00 - 15:00 B1 L3 R3119 Indoor localization Ultrasonic-based Localization Signal processing High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.
Geometric Sensor Fusion for Pose Estimation on Riemannian Manifolds Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech) Sep 18, 11:00 - 12:00 B1 L3 R3119 This seminar introduces a novel geometric sensor fusion framework that integrates inertial measurements with acoustic ranging on Riemannian manifolds to achieve robust, high-accuracy pose estimation for autonomous systems in GPS-denied environments.
Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech)
Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech) Dec 16, 14:00 - 15:00 B1 L3 R3119 Indoor localization Ultrasonic-based Localization Signal processing High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.
Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech)
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