Assistant Professor

Department of Aeronautical and Aviation Engineering
The Hong Kong Polytechnic University
Research interests:  GNSS multipath mitigation, Deep learning assisted GNSS, Signal propagation modeling, Remote Sensing, Indoor positioning and indoor navigation

Speech title: Leveraging GNSS to Urban Environment Sensing

Abstract: Global Navigation Satellite System (GNSS) has become a fundamental infrastructure for positioning, navigation, and timing, offering high availability and low-cost positioning for a wide range of civil applications. In open-sky environments, GNSS can achieve meter-level to even centimeter-level accuracy. However, in dense urban areas, GNSS signals are frequently blocked, reflected, diffracted, and scattered by buildings, vegetation, and other surrounding objects, leading to multipath and non-line-of-sight receptions that can severely degrade positioning accuracy. Traditionally, these effects are regarded as harmful errors to be detected, excluded, or mitigated. However, the same degraded measurements also carry information about how GNSS signals interact with the surrounding environment. Thus, NLOS reception and multipath effects are not only error sources, but also additional observations that can be exploited for urban environment sensing.
This seminar will introduce the developments in exploring the new potential of extending GNSS from positioning to urban sensing, via two complementary approaches. On the one hand, with the rapid developments on artificial intelligence, deep learning networks show excellent capabilities on extracting hidden environmental features from GNSS measurements, enabling context awareness and fine-grained environment perception in complex urban scenarios. On the other hand, as a popular approach for remote sensing, GNSS interferometric reflectometry (GNSS-IR) analyzes multipath-induced signal oscillations to retrieve geometric information of natural phenomena, which has the potential to be extended to urban environments. GNSS-IR can be used to retrieve the precise distance information of the surrounding buildings. Together, these approaches demonstrate the potential of GNSS to sense urban environments, opening new opportunities for GNSS to wider applications.  
Bio:
Guohao Zhang received his bachelor’s degree in mechanical engineering and automation from University of Science and Technology Beijing, China, in 2015. He received his master’s degree in Mechanical Engineering and his Ph.D. degree in Aeronautical and Aviation Engineering from The Hong Kong Polytechnic University, Hong Kong, in 2017 and 2022, respectively. He is currently an Assistant Professor with the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University. His research interests include GNSS positioning in challenging environments, machine-learning-aided GNSS, signal propagation modelling, indoor positioning, and remote sensing.