Tomoya Yamashita
Papers
4
Total Citations
17
H-Index
3
About
Tomoya Yamashita is a researcher specializing in indoor localization systems, wireless network-based positioning, and probabilistic estimation methods for mobile devices and robotics. His work addresses one of the fundamental challenges in modern computing: accurately determining the physical location of mobile clients — whether personal devices or autonomous robots — within complex indoor environments where GPS signals are unreliable or unavailable. Yamashita's most significant contributions center on leveraging existing public wireless LAN infrastructure for multistory building localization, eliminating the need for dedicated positioning hardware. His most-cited work (2011, 9 citations) introduced a probabilistic framework using Sparse Bayesian Learning to achieve robust localization across multiple building floors — a technically demanding problem due to signal variability and vertical ambiguity. He further advanced the field by incorporating state space frameworks for tracking mobile clients dynamically over time, published in 2012. Collectively accumulating 17 citations across his key publications, Yamashita's research has contributed practical, scalable approaches to indoor positioning that carry clear implications for smart building services, mobile robotics navigation, and location-aware applications. His consistent focus on stochastic and probabilistic methodologies demonstrates a rigorous mathematical approach to solving real-world spatial computing challenges.
Research Focus
Key Achievements
Top Papers
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