The Design of an Energy-saving Vector-based GNSS/INS Deep Integration System

Xinhua Tang, Xuehao Yu, Xin Chen, Haiying Wang

Abstract:	With the development of portable navigation gadgets, power-saving becomes a competitive factor for both mass-market products and dedicated ones. Considering a deep GNSS/INS integrated navigation system as background, on the receiver side, based on previous work, a Quasi-Kalman is proposed as pre-filter inside the tracking channel, which can provide accurate parameters estimation including code delay, frequency, and carrier phase. While one the INS side, only a low-pass filter is used in Dead-Reckoning process to facilitate the evaluation of power-saving strategy. With respect to the intermittent working pattern, a new duty-cycle strategy taking advantage of INS is proposed and tested in real scenario, the main highlight of the new proposed strategy is that the initialization of tracking process in every wake-up exploits the information provided by INS, which can largely extend the sleep period to save the energy without impacting the normal working of the integrated system. The result shows that the validity of the proposed power-saving strategy, with the setting of the experiment, it can save the energy up to 80% only considering the GNSS receiver. Meanwhile, it can be concluded that the setting of sleep period and active period mainly depends on the quality of IMU. Finally, the future work including the hardware implementation and indepth analysis of the deep GNSS/INS integrated system is also proposed.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	541 - 548
Cite this article:	Tang, Xinhua, Yu, Xuehao, Chen, Xin, Wang, Haiying, "The Design of an Energy-saving Vector-based GNSS/INS Deep Integration System," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 541-548. https://doi.org/10.33012/2017.15296
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