Walker: Continuous and Precise Navigation by Fusing GNSS and MEMS in Smartphone Chipsets for Pedestrians

Abstract

The continual miniaturization of mass-market sensors built in mobile intelligent terminals has inspired the development of accurate and continuous navigation solution for portable devices. With the release of Global Navigation Satellite System (GNSS) observations from the Android Nougat system, smartphones can provide pseudorange, Doppler, and carrier phase observations of GNSS. However, it is still a challenge to achieve the seamless positioning of consumer applications, especially in environments where GNSS signals suffer from a low signal-to-noise ratio and severe multipath. This paper introduces a dedicated android smartphone application called Walker that integrates the GNSS navigation solution and MEMS (micro-electromechanical systems) sensors to enable continuous and precise pedestrian navigation. Firstly, we introduce the generation of GNSS and MEMS observations, in addition to the architecture of Walker application. Then the core algorithm in Walker is given, including the time-differenced carrier phase improved GNSS single-point positioning and the integration of GNSS and Pedestrian Dead Reckoning (PDR). Finally, the Walker application is tested and the observations of GNSS and MEMS are assessed. The static experiment shows that, with GNSS observations, the RMS (root mean square) values of east, north, and up positioning error are 0.49 m, 0.37 m, and 1.01 m, respectively. Furthermore, the kinematic experiment verifies that the proposed method is capable of obtaining accuracy within 1-3 m for smooth and continuous navigation. scenarios [4,5]. Furthermore, pedestrian navigation systems (PNSs) are expected to provide continuous positioning and enhanced navigation performance under such cases. Based on fusing measurements of IMUs and magnetometers, the characteristics of human gait can be exploited in pedestrian dead reckoning (PDR) algorithms, which are the primary component of PNSs to continuous relative position [6,7]. A PDR system performs three main tasks: step detection, step length estimation, and heading determination [8,9]. However, the performance of a PDR system would significantly decrease with the increase in recursion time, due to sensor noise and model errors. Therefore, the absolute positioning accuracy of GNSS and the relative positioning accuracy of PDR complement each other in a smartphone, enabling the smartphone to provide continuous positioning services.With the release of GNSS observations from the Android Nougat system, smartphones, such as Huawei P10 and Samsung S8 models, can provide pseudorange, Doppler, and carrier phase observations of GNSS systems [10]. Moreover, a new generation of mass-market chips based on dual frequency measurements is next to be commercialized [11]. These released data enable us to develop advanced algorithms to increase positioning accuracy. Using a single-frequency PPP model and an extern SBAS correction, sub-meter accuracy can be reached with android GNSS observations [12]. The decimeter positioning accuracy can also be obtained through rapid-static sur...

Real-time GNSS precise point positioning with smartphones for vehicle navigation

“…In Zhang et al (2018), the quality of the raw Global Positioning System (GPS) measurements from a Google Nexus 9 tablet was analyzed by comparing the C/N 0 and single-differenced code residuals with the corresponding measurements from geodetic-grade receivers, and the pseudorange rate, phase rate, and Doppler data from the smart device were also examined for the purpose of deriving velocity. The similar analyses about the data quality of the raw GNSS measurements from smartphones can also be found in Lu et al (2018) and Zhu et al (2019). Håkansson (2019) further assessed the multipath effect of GNSS observations with smart devices on positioning performance.…”

Conservative Sensor Error Modeling Using a Modified Paired Overbound Method and its Application in Satellite-Based Augmentation Systems

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HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

“…Recently, however, work has shown that the use of phase measurements by time difference can be more effective and provide a very accurate velocity estimate (Pierluigi, Antonio, Salvatore, & Salvatore, 2015). Thus, TDCP can be used to correct INS measurements and errors or in a PDR approach to correct walking direction estimation error but also the step-length error (Angrisano, Vultaggio, Gaglione, & Crocetto, 2019;Feng et al, 2019;Tao, Zhang, Zhu, Wang, & Teng, 2018).…”