|The Precise Point Positioning (PPP) technique for Global Navigation Satellite Systems (GNSS) is applied widely for scientific applications that require sub-metre-level accuracy with few obstructions. However, considering the technique has advantages such as being self-contained, and given the requirements for emerging applications such as autonomous vehicles, PPP is expanding into mass-market applications at the consumer level. As the new generation of low-cost, multi-constellation and dual-frequency receivers enter the market, PPP has a promising future in the new applications. However, the technique suffers from inherent disadvantages of GNSS-based technology. Obstructive environments such as urban canyon or complex overpasses downgrade the solution due to signal obstruction, greater multipath errors and poor satellite geometry. With enough sky obstruction, current GNSS-PPP algorithms may not be able to produce a solution at all. This study presents a solution to reduce the number of satellites required in such scenarios and improves the navigation solution given poor geometry by implementing a clock coasting method to PPP and using a GNSS receiver aided by a chip-scale atomic clock (CSAC). Results show an improvement of 0.16 m of rms error in suburban driving scenarios using the CSAC-aided receiver versus an identical receiver without external clock. Through simulation of signal obstructions, the CSAC-aided receiver with clock-coasting produces kinematic solutions at sub-metre level of errors. In a more extreme case, it produces a solution of sub-metre level accuracy, where traditionally, the method fails to produce a converged solution at all.
Proceedings of the 2020 International Technical Meeting of The Institute of Navigation
January 21 - 24, 2020
Hyatt Regency Mission Bay
San Diego, California
|521 - 537
|Cite this article:
Yang, Sihan, Bisnath, Sunil, "GNSS PPP Navigation in Obstructed Environments Using Clock Coasting," Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2020, pp. 521-537.
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