High-precision velocity determination using mass-market Android GNSS measurements in the case of anomalous clock variations

The increasingly improved performance of mass-market GNSS chipsets is driving smartphone GNSS positioning or velocimetry as a low-cost GNSS solution for high-precision vibration monitoring applications. In this study, the Android GNSS velocity measurement performance of mass-market smartphones was evaluated. Based on the smartphone GNSS data generated by the Geo + + RINEX Logger, we found smartphone anomalous clock variations, as evidenced by the biases between the TDCP-estimated and Doppler-estimated receiver clock drifts, as well as frequent jumps. As a result, the traditional Doppler and TDCP combination method that estimates the receiver TDCP clock drift as the same parameter as the Doppler clock drift is no longer applicable. To solve this problem, we provide two strategies, including inter-satellite differencing and dual clock drift estimation. The results of static and shake table experiments show that the traditional combination method solutions for smartphones contain many outliers, with the root mean square (RMS) of the horizontal velocity measurement errors exceeding 1 m/s. In contrast, using the inter-satellite differencing and dual clock drift estimation strategies, the velocity error RMS are both reduced to less than 1 cm/s. For a representative Huawei P40 smartphone, their static experimental horizontal velocity error RMS is 0.27 and 0.26 cm/s, respectively, and their mean velocity error RMS of six shaking tests are 0.52 and 0.40 cm/s, respectively. In addition, the causes of this anomalous clock variation are further discussed and the noise characteristics of Android multi-constellation multi-frequency Doppler and TDCP observations are analyzed. These results are encouraging and show that we can obtain a few mm/s velocities using inexpensive smartphones or their embedded GNSS chipsets. In this case, there is a cost-effective solution for implementing a dense network of monitoring arrays or developing low-cost monitoring instruments with integrated GNSS.