Anup Dhital

University of Calgary, Universitat Politècnica de Catalunya

Papers

Total Citations

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About

Anup Dhital is a researcher whose work centers on the critical challenge of accurate indoor localization and tracking—a problem where conventional satellite navigation systems like GNSS fail. His primary research areas include Bayesian filtering, sensor fusion, and wireless sensor networks, with a specific focus on developing robust algorithms for dynamic systems. Dhital’s major contribution lies in his experimental validation of various Bayesian techniques, including the novel cost-reference particle filter, for tracking moving devices in GNSS-denied environments. His most-cited paper, "Bayesian filtering for indoor localization and tracking in wireless sensor networks" (2012, 19 citations), demonstrates the practical application of these filters using ultra-wideband sensors on a robotic platform. Complemented by his earlier work (2010, 6 citations) and his M.Sc. thesis (2010, 6 citations), Dhital’s research provides a foundational comparison of sequential Monte Carlo methods, offering a clear pathway for improving localization accuracy in complex indoor and urban settings. His work is essential reading for students and engineers tackling real-world tracking challenges.