Research
My research interests span Low Earth Orbit (LEO) satellites Precise Orbit Determination (POD), and computational navigation systems. I specialize in developing algorithms for GNSS-based atmospheric sensing, automated satellite data processing, and intelligent positioning systems. My work bridges theoretical research with practical implementations, contributing to advances in navigation systems. Currently, I focus on solving real-world challenges in autonomous navigation, and machine learning applications.
An Algorithm for Estimating Thermospheric Density Using LEO Dual-Frequency GNSS Receivers: Design, Uncertainty Analysis, and Validation
38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025)
Developed and validated a novel algorithm for estimating thermospheric density using dual-frequency GNSS receivers on Low Earth Orbit (LEO) satellites. The algorithm addresses challenges in satellite drag modeling by extracting along-track total acceleration from precise orbit determination, computing air-drag acceleration, and introducing separate drag coefficients for satellite main body and solar panels. Demonstrated successful validation using COSMIC-2 satellites with comparison against the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model during geomagnetic storm conditions in February 2022.
Indoor Navigation with AR using Visual-Based Localization and RSSI
Combined Visual-Based Localization with Received Signal Strength Indicator for an Indoor Navigation Application. Explored Bayesian Filters for Sensor Fusion. Incorporated multiuser experience where an indoor space is scanned for its visual & RSSI landmarks, and this data is stored in Firebase and it's retrievable for all other users within the same space for navigation.