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About

Y. B. Zhao’s research centers on advancing indoor positioning and localization technologies, particularly in challenging wireless network environments. Their major contributions lie in developing robust algorithms to mitigate the effects of non-line-of-sight (NLOS) and multipath interference on ranging measurements. Zhao introduced the Recursively Bounded Grid-Based Filter (RBGF) for efficient indoor position tracking, which balances computational demands with accuracy for low-power network nodes. They also proposed the Geometric Bayesian filter (GeoF), a novel approach for sequential position estimation in mixed LOS/NLOS conditions. These works, published in 2014, have collectively garnered over 24 citations, demonstrating their influence in the field. Zhao’s experimental evaluation of indoor localization algorithms provides critical insights into the practical performance of lateration techniques, making their research foundational for engineers and researchers developing resilient positioning systems for smart buildings, robotics, and IoT applications.