Title: Adaptive Algorithm for Low-cost Single-frequency Positioning in Urban Environments: Design and Performance Analysis
Author(s): Ivan Smolyakov and Richard B. Langley
Published in: Proceedings of the 2018 International Technical Meeting of The Institute of Navigation
January 29 - 1, 2018
Hyatt Regency Reston
Reston, Virginia
Pages: 142 - 153
Cite this article: Smolyakov, Ivan, Langley, Richard B., "Adaptive Algorithm for Low-cost Single-frequency Positioning in Urban Environments: Design and Performance Analysis," Proceedings of the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, January 2018, pp. 142-153.
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Abstract: A new generation of mobile platforms equipped with chips allowing continuous carrier-phase tracking is to put applications based on localization at the next level. Whether in transportation, pedestrian navigation or safety of life services, a robust position determination is required in various environments including cities. Navigation in urban environments is significantly challenged by signal degradation. Typical urban scenarios result in blocked signals, reception of non-line-of-sight (NLOS) signals and multipath-contaminated signals. Low-cost single-frequency equipment suffers the most from such effects as a consequence of hardware limitations, while also being affected by potentially poor satellite geometry. This paper addresses the challenge for mobile platforms equipped with low-cost single-frequency receivers and patch antennas to efficiently utilize all GNSS signals available. Various techniques attempt to minimize the impact of NLOS and multipath on a final solution: weighting based on elevation angle of a satellite and signal-to-noise ratio of its signal, as well as exclusion of certain satellites from processing selecting the most consistent set of satellites. Here, the approach is explored, combining aforementioned methods with automatic stochastic model adjustment. Signal degradation demonstration and algorithm testing was performed on 1Hz GPS/GLONASS static and kinematic datasets collected in an urban environment. Our proposed algorithm yielded sub-meter level positioning and showed 10% accuracy improvement compared to regular weighting and satellite-exclusion-based algorithms.