I work at the intersection of academic research and industry, focusing on intelligent localization, multi-sensor fusion, and embedded AI. My research connects algorithm development with embedded system design to bring positioning and sensing capabilities to wearable and IoT platforms.
My broader research interests span sensing and navigation for mobile and autonomous systems, with an emphasis on real-time state estimation, embedded intelligence, and hardware–software co-design.
Intelligent localization & navigation · Multi-sensor fusion & on-device AI · Embedded systems & industrial IoT
Our preprint on continuous indoor–outdoor positioning for location-based safety management is available on arXiv. Details
I received my Ph.D. in Computer Science from the Korea Advanced Institute of Science and Technology (KAIST). Details
Our work on PDRS, a self-contained inertial positioning module for wearable navigation, was published in the proceedings of IEEE SMC 2025. Details
arXiv · Preprint
A wearable–cloud positioning architecture that combines GNSS, inertial, and barometric measurements with map-aware fusion for continuous indoor–outdoor localization. The system supports geofencing and real-time monitoring for industrial safety under device power and communication constraints.
IEEE SMC
Presents PDRS, a self-contained inertial positioning module that performs real-time sensor fusion on an embedded processor and provides position estimates through I²C, SPI, and UART. Hardware–software co-design simplifies integration into low-power wearable and IoT platforms.
IEEE SMC
An embedded pedestrian dead reckoning algorithm with acceleration-differential step detection and dynamic gyroscope bias updates. Processing inertial measurements directly on K-Tag reduces communication with the positioning server while supporting low-latency relative localization.
For research collaboration and industry R&D inquiries, please contact vuanhvan@kaist.ac.kr.