Multi-sensor Fusion Algorithm Based on GPS/MEMS-IMU Tightly Coupled for Smartphone Navigation Application

Wei Liu, Bingcheng Liu and Xiao Chen

Abstract:	In order to improve the navigation performance of smartphone in GPS signal harsh environment, this paper proposes a multisensor fusion navigation algorithm based on GPS / MEMS-IMU tight combination (BTC-MSFA) which is proved to be effective by experiment. First of all, the precision level of MEMS gyroscope and accelerometer embedded in smartphone was analyzed. Then, a GPS/INS product which can output high precision position reference data, GPS raw data and MEMS-IMU raw data was choose. Finally, experiments were carried out to compare BTC-MSFA positioning results and benchmarks. The result of the experiment shows that: 1) the positioning results of BTC-MSFA are consistent with the benchmark in the road where the navigation satellite visual condition is good. The deviation of BTC-MSFA results and benchmark is less than 0.5m 2) The BTC-MSFA positioning results are smooth and continuous, and the deviation from the benchmark is less than 10m in the case where the navigation satellite signal is blocked, the number of visible satellites are frequently changed from 0 to 8 and meanwhile the number of visible satellites are less than 4 at most of the time 3) the deviation from the benchmark was greater than 30 meters, when the number of visible satellites continues to be less than 1 in dozens of seconds.4) BTC-MSFA still can provide positioning results when the car passing under the viaduct in a short time.
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:	2973 - 2980
Cite this article:	Liu, Wei, Liu, Bingcheng, Chen, Xiao, "Multi-sensor Fusion Algorithm Based on GPS/MEMS-IMU Tightly Coupled for Smartphone Navigation Application," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2973-2980. https://doi.org/10.33012/2017.15167
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