主讲人：Sunil Bisnath 教授 加拿大约克大学
地 点：卫星导航定位技术研究中心 13楼会议室
主请人： 耿江辉 教 授
The next generation of low-cost, multi-frequency, multi-constellation GNSS receivers, boards, chips and antennas are now quickly entering the market. The presented work provides an investigation into the potential for mass-market, high-accuracy positioning. A set of experiments have been carried-out, collecting measurements from a number of low-cost, dual-frequency, multi-constellation GNSS boards, chips and antennas. In order to be comprehensive and realistic, these static and kinematic experiments were conducted in benign, typical, suburban and urban environments.
The Precise Point Positioning (PPP) GNSS measurement processing mode has been used for measurement processing. While real-time kinematic (RTK) and network RTK dominate urban and suburban markets, it was deemed of great scientific interest to assess the PPP performance with these hardware options with and without additional local augmentation corrections.
Analysis of the raw measurements illustrates a) some significant measurement gaps with some sensors, b) the lower signal availability and c) the weaker signal strength from the chip/antenna combinations as compared to geodetic quality instrumentation, d) high pseudorange multipath and noise, and e) some interesting measurement curiosities. Results for new smartphone sensors show positioning performance is typically at the few dm-level with limited convergence period – 1 to 2 orders of magnitude better than standard point positioning. The GNSS chips and boards combined with higher-quality antennas produce positioning performance approaching geodetic quality. And under ideal conditions, phase ambiguities are resolvable. However, there are a number of caveats to these performance assessments, as consistence of results is lower for all of these new sensors as compared to geodetic hardware.
These results are very promising for the use of PPP and RTK in next-generation GNSS sensors for smartphone, vehicle, Internet of things (IoT), etc. applications. Future work includes further tuning of the measurement processing, ambiguity resolution, and use of AI techniques for measurement filtering.
Dr. Sunil Bisnath is a Full Professor in the Department of Earth and Space Science and Engineering at York University in Toronto, Canada. He has over 25 years of experience working with GNSS. His research interests include precise GNSS positioning and navigation algorithms and applications. Previous to York University, Professor Bisnath held the positions of geodesist at the Harvard-Smithsonian Center for Astrophysics in Boston, Massachusetts and assistant research scientist at the University of Southern Mississippi, NASA Stennis Space Center, Mississippi. He holds an Honours B.Sc. and M.Sc. in Surveying Science from the University of Toronto and a Ph.D. in Geodesy and Geomatics Engineering from the University of New Brunswick.