# Evaluation of Real-Time Kinematic Positioning and Deformation Monitoring Using Xiaomi Mi 8 Smartphone

^{*}

## Abstract

**:**

## 1. Introduction

#### Our Contribution

## 2. Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis

#### 2.1. Data Collection

#### 2.2. Data Quality Analysis

#### 2.2.1. Dual-Frequency C/N0 Analysis

#### 2.2.2. Dual-Frequency Cycle Slip Analysis

## 3. Data Processing and Analysis for Smartphone Real-Time Kinematic (RTK) Positioning

#### 3.1. Data Processing for Smartphone RTK Positioning

#### 3.1.1. Stochastic Model and Cycle-Slip Detection

#### 3.1.2. Partial Ambiguity Resolution

#### 3.2. Evaluation of Data Processing

#### 3.3. Evaluation of Ambiguity Resolution for Different Observation Combinations

#### 3.4. Analysis of RTK-Positioning Convergence Time by Segment

## 4. Evaluation of Dynamic Deformation Monitoring Using Smartphone

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- The European GNSS Agency. GNSS Market Report 2017; The European GNSS Agency: Prague, Czech Republic, 2017. [Google Scholar]
- Paziewski, J. Recent advances and perspectives for positioning and applications with smartphone GNSS observations. Meas. Sci. Technol
**2020**, 31, 091001. [Google Scholar] [CrossRef] - Aggrey, J.; Bisnath, S.; Naciri, N.; Shinghal, G.; Yang, S. Multi-GNSS precise point positioning with next-generation smartphone measurements. J. Spatial Sci.
**2019**, 65, 79–98. [Google Scholar] [CrossRef] - White Paper on Using GNSS Raw Measurements on Android Devices; European GNSS Agency: Prague, Czech Republic, 2017.
- Zhang, X.; Tao, X.; Zhu, F.; Shi, X.; Wang, F. Quality assessment of GNSS observations from an Android N smartphone and positioning performance analysis using time-differenced filtering approach. GPS Solut.
**2018**, 22, 70. [Google Scholar] [CrossRef] - Liu, W.; Shi, X.; Zhu, F.; Tao, X.; Wang, F. Quality analysis of multi-GNSS raw observations and a velocity-aided positioning approach based on smartphones. Adv. Space Res.
**2019**, 63, 2358–2377. [Google Scholar] [CrossRef] - Li, G.; Geng, J. Characteristics of raw multi-GNSS measurement error from Google Android smart devices. GPS Solut.
**2019**, 23, 90. [Google Scholar] [CrossRef] - Robustelli, U.; Baiocchi, V.; Pugliano, G. Assessment of dual frequency GNSS observations from a Xiaomi Mi 8 Android smartphone and positioning performance analysis. Electronics
**2019**, 8, 91. [Google Scholar] [CrossRef] [Green Version] - Realini, E.; Caldera, S.; Pertusini, L.; Sampietro, D. Precise GNSS positioning using smart devices. Sensors
**2017**, 17, 2434. [Google Scholar] [CrossRef] [Green Version] - Dabove, P.; Di Pietra, V. Single-baseline RTK positioning using dual-frequency GNSS receivers inside smartphones. Sensors
**2019**, 19, 4302. [Google Scholar] [CrossRef] [Green Version] - Dabove, P.; Di Pietra, V. Towards high accuracy GNSS real-time positioning with smartphones. Adv. Space Res.
**2019**, 63, 94–102. [Google Scholar] [CrossRef] - Liu, J.; Deng, C.; Tang, W. Review of GNSS ambiguity validation theory. Geomat. Inf. Sci. Wuhan Univ.
**2014**, 39, 1009–1016. [Google Scholar] - Wanninger, L.; Heßelbarth, A. GNSS code and carrier phase observations of a Huawei P30 smartphone: Quality assessment and centimeter-accurate positioning. GPS Solut.
**2020**, 24, 1–9. [Google Scholar] [CrossRef] [Green Version] - Geng, J.; Li, G. On the feasibility of resolving Android GNSS carrier phase ambiguities. J. Geod.
**2019**, 93, 2621–2635. [Google Scholar] [CrossRef] - Bochkati, M.; Sharma, H.; Lichtenberger, C.A.; Pany, T. Demonstration of fused RTK (fixed) + inertial positioning using Android smartphone sensors only. In Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 20–23 April 2020; pp. 1140–1154. [Google Scholar]
- Gao, R.; Xu, L.; Zhang, B.; Liu, T. Raw GNSS observations from Android smartphones: Characteristics and short-baseline RTK positioning performance. Meas. Sci. Technol.
**2021**, 32, 084012. [Google Scholar] [CrossRef] - Uradziński, M.; Bakuła, M. Assessment of static positioning accuracy using low-cost smartphone GPS devices for geodetic survey points’ determination and monitoring. Appl. Sci.
**2020**, 10, 5308. [Google Scholar] [CrossRef] - Tomaštík, J.; Chudá, J.; Tunák, D.; Chudy, F.; Kardoš, M. Advances in smartphone positioning in forests: Dual-frequency receivers and raw GNSS data. For. Int. For. Res.
**2020**, 94, 292–310. [Google Scholar] [CrossRef] - Lu, L.; Ma, L.; Liu, W.; Wu, T.; Chen, B. A triple checked partial ambiguity resolution for GPS/BDS RTK positioning. Sensors
**2019**, 19, 5034. [Google Scholar] [CrossRef] [Green Version] - Logging of GNSS Raw Data on Android. Available online: http://www.geopp.de/logging-of-gnss-rawdata-on-android/ (accessed on 10 January 2020).
- Netthonglang, C.; Thongtan, T.; Satirapod, C. GNSS Precise Positioning determinations using smartphones. In Proceedings of the 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Bangkok, Thailand, 11–14 November 2019; pp. 401–404. [Google Scholar]
- Guo, L.; Wang, F.; Sang, J.; Lin, X.; Gong, X.; Zhang, W. Characteristics analysis of raw multi-GNSS measurement from Xiaomi Mi 8 and positioning performance improvement with L5/E5 frequency in an urban environment. Remote Sens.
**2020**, 12, 744. [Google Scholar] [CrossRef] [Green Version] - Zhao, S.; Cui, X.; Guan, F.; Lu, M. A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS. Sensors
**2014**, 14, 15415–15433. [Google Scholar] [CrossRef] - Hartinger, H.; Brunner, F.K. Variances of GPS phase observations: The SIGMA-ɛ model. GPS Solut.
**1999**, 2, 35–43. [Google Scholar] [CrossRef] - Wang, L.; Li, Z.; Wang, N.; Wang, Z. Real-time GNSS precise point positioning for low-cost smart devices. GPS Solut.
**2021**, 25, 69. [Google Scholar] [CrossRef] - Wen, Q.; Geng, J.; Li, G.; Guo, J. Precise point positioning with ambiguity resolution using an external survey-grade antenna enhanced dual-frequency android GNSS data. Measurement
**2020**, 157, 107634. [Google Scholar] [CrossRef] - Teunissen, P.J.G. The least-squares ambiguity decorrelation adjustment: A method for fast GPS integer ambiguity estimation. J. Geod.
**1995**, 70, 65–82. [Google Scholar] [CrossRef] - Takasu, T.; Yasuda, A. Development of the low-cost RTK-GPS receiver with an open source program package RTKLIB. In Proceedings of the International symposium on GPS/GNSS, International Convention Center, Jeju, Korea, 21–24 October 2014; pp. 4–6. [Google Scholar]
- Takasu, T.; Yasuda, A. Kalman-filter-based integer ambiguity resolution strategy for long-baseline RTK with ionosphere and troposphere estimation. In Proceedings of the 23rd International Technical Meeting of the Satellite Division of the Institute-of-Navigation (ION GNSS-2010), Portland, OR, USA, 21–24 September 2010; pp. 161–171. [Google Scholar]
- Liu, Y.; Ye, S.; Song, W.; Li, Q. Estimating the orbit error of BeiDou GEO satellites to improve the performance of multi-GNSS PPP ambiguity resolution. GPS Solut.
**2018**, 22, 1–14. [Google Scholar] [CrossRef] - Mohammed, A.G.; Mosbeh, R.K.; Mostafa, M.R.; Zaki, M.Z. Improving Precise Point Positioning Convergence Time through TEQC Multipath Linear Combination. J. Surv. Eng.
**2018**, 144, 04018002. [Google Scholar] - Hou, H.; Wang, Y.; Kuang, C. Feasibility analysis on dynamic deformation monitoring of high-rise buildings by low-cost GNSS receivers. J. Navig. Position
**2019**, 7, 94–98. [Google Scholar]

**Figure 1.**Diagram of global navigation satellite system (GNSS) antenna phase centre in Xiaomi Mi 8 smartphone [21].

**Figure 3.**C/N0 of different types of observations. (

**a**) C/N0 sequence. (

**b**) Statistical results of mean C/N0.

**Figure 4.**Satellite visibility and cycle slips (red plus signs) of four constellations on two frequency bands: (

**a**) global positioning system (GPS) L1/GLONASS G1/Galileo E1/BDS B1; (

**b**) GPS L5/Galileo E5a. (PRN, pseudorandom noise).

**Figure 5.**Statistical results of cycle slips for four constellations. (

**a**) Ratio of cycle slips to number of observations. (

**b**) Ratio of cycle slips to number of cycle slips in various C/N0 ranges.

**Figure 13.**Results of segmented solutions over 1 h intervals for data acquired over 3 days. (

**a**) Dataset 1 collected on 15 November 2020, (

**b**) dataset 2 collected on 11 November 2020, and (

**c**) dataset 3 collected on 14 November 2020.

East (m) | North (m) | Up (m) |
---|---|---|

−0.027 | −0.015 | +0.191 |

Parameter | Value |
---|---|

Mode | Kinematic |

Cut-off elevation | 10° |

C/N0 threshold | 20 dB-Hz |

Stochastic model | elevation + C/N0 |

Carrier-phase noise $({a}_{0}$$,{b}_{0}$): 0.003 m | |

Code-carrier error ratio: 300:1 | |

Cycle-slip detection | Loss-of-lock indicator/Doppler/code-carrier combination |

Ambiguity resolution mode | Continuous/fix-and-hold + partial ambiguity resolution |

Minimum lock to fix ambiguity | 5 |

C/N0 threshold to fix ambiguity | 30 dB-Hz |

Ratio threshold to fix ambiguity | 3.0 |

**Table 3.**Real-time differential root-mean-square (RMS) errors of different stochastic models for three directions.

Stochastic Model | East (m) | North (m) | Up (m) |
---|---|---|---|

Elevation | 2.26 | 2.08 | 5.29 |

SIGMA-ε | 2.36 | 2.25 | 5.50 |

Unified-CNM | 2.48 | 2.53 | 5.99 |

Individual-CNM | 2.00 | 2.00 | 4.93 |

Stochastic Model | Convergence Time (min) | RMS Error after Convergence | ||
---|---|---|---|---|

East (m) | North (m) | Up (m) | ||

Elevation | 44.1 | 0.067 | 0.029 | 0.064 |

SIGMA-ε | 103.9 | 0.057 | 0.037 | 0.057 |

Unified-CNM | 76.0 | 0.040 | 0.036 | 0.064 |

Individual-CNM | 44.7 | 0.046 | 0.031 | 0.067 |

Fixed Mode | Fixing Rate (%) | Error-Fixing Rate (%) | Convergence Time (min) | RMS Error after Convergence | ||
---|---|---|---|---|---|---|

East (m) | North (m) | Up (m) | ||||

Float ambiguity | – | – | 35.0 | 0.035 | 0.020 | 0.043 |

Full resolution | 63.4 | 0 | 35.0 | 0.018 | 0.014 | 0.031 |

Partial resolution | 90.4 | 5.3 | 25.1 | 0.012 | 0.012 | 0.026 |

**Table 6.**Solutions for single-frequency observation combinations (G, GPS; R, GLONASS; E, Galileo; C, BDS).

Observation Combination | Mean Number of Satellites | Fixing Rate (%) | Error-Fixing Rate (%) | Convergence Time (min) | RMS Error after Convergence | ||
---|---|---|---|---|---|---|---|

East (m) | North (m) | Up (m) | |||||

G | 6.6 | 28.3 | 28.3 | Non-convergent | – | – | – |

C | 6.5 | 3.3 | 3.2 | Non-convergent | – | – | – |

G/C | 13.1 | 55.6 | 5.9 | 71.7 | 0.011 | 0.015 | 0.021 |

G/E | 10.0 | 90.8 | 5.3 | 25.1 | 0.012 | 0.011 | 0.025 |

G/E/C | 16.5 | 60.7 | 12.4 | 58.3 | 0.012 | 0.009 | 0.019 |

G/R/E/C | 21.2 | 49.7 | 0.8 | 36.3 | 0.023 | 0.009 | 0.022 |

G/C (no GEO) | 11.2 | 80.3 | 0 | 23.0 | 0.013 | 0.016 | 0.026 |

G/E/C (no GEO) | 14.6 | 85.7 | 0 | 32.5 | 0.013 | 0.012 | 0.017 |

G/E/C (no GEO/float C) | 14.6 | 88.5 | 2.9 | 25.0 | 0.012 | 0.010 | 0.022 |

**Table 7.**Solutions of dual-frequency combinations. The mean number of satellites only includes dual-frequency satellites.

Observation Combination | Mean Number of Satellites | Fixing Rate (%) | Error-Fixing Rate (%) | Convergence Time (min) | RMS Error after Convergence | ||
---|---|---|---|---|---|---|---|

East (m) | North (m) | Up (m) | |||||

G L1/L5 | 2.9 | 22.4 | 5.4 | Non-convergent | – | – | – |

G L1/L5 (float L5) | 59.1 | 20.4 | Non-convergent | – | – | – | |

G L1/L5 + E E1/E5a | 5.8 | 10.6 | 8.7 | Non-convergent | – | – | – |

G L1/L5 + E E1/E5a (float L5/E5a) | 88.0 | 3.6 | 30.8 | 0.013 | 0.011 | 0.024 |

Dataset | Segment | Convergence Time (min) | Mean Number of G/E Satellites | Mean Number of Candidate Ambiguities | Cycle-Slip Rate (%) | Pseudo-Range RMS Residual (m) |
---|---|---|---|---|---|---|

1 | 1 | 25.0 | 10.4 | 5.4 | 1.03 | 5.05 |

2 | 12.8 | 10.9 | 7.7 | 0.12 | 4.37 | |

3 | 17.3 | 8.7 | 3.7 | 0.35 | 5.07 | |

2 | 1 | 13.0 | 11.2 | 8.0 | 0.37 | 5.87 |

2 | 14.5 | 11.4 | 7.2 | 0.07 | 5.04 | |

3 | Not fixed | 11.1 | 0.4 | 0.35 | 5.28 | |

3 | 1 | Not fixed | 8.5 | 0.2 | 1.34 | 4.74 |

2 | 33.9 | 8.2 | 4.0 | 0.17 | 4.66 | |

3 | 13.3 | 9.3 | 7.2 | 0.31 | 4.60 |

Moving Time (GPST) | North Displacement (mm) | East Displacement (mm) |
---|---|---|

08:00 (start) | 0 | 0 |

08:30 | 0 | 0 |

09:00 | −20 | −20 |

09:30 | −20 | −20 |

10:00 | 20 | 20 |

10:30 | 20 | 20 |

11:00 (end) | 0 | 0 |

Segment | Convergence Time (min) | RMS Error of Fixed Solution | Mean Number of Candidate Ambiguities | Cycle-Slip Rate (%) | Pseudo-Range RMS Residual (m) | ||
---|---|---|---|---|---|---|---|

East (m) | North (m) | Up (m) | |||||

1 | 5.0 | 0.021 | 0.004 | 0.023 | 6.0 | 0.0 | 2.15 |

2 | 3.9 | 0.008 | 0.004 | 0.011 | 7.6 | 0.0 | 2.10 |

3 | 3.2 | 0.004 | 0.004 | 0.012 | 7.4 | 0.0 | 2.26 |

4 | 1.5 | 0.003 | 0.004 | 0.007 | 8.9 | 0.0 | 2.12 |

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**MDPI and ACS Style**

Zeng, S.; Kuang, C.; Yu, W.
Evaluation of Real-Time Kinematic Positioning and Deformation Monitoring Using Xiaomi Mi 8 Smartphone. *Appl. Sci.* **2022**, *12*, 435.
https://doi.org/10.3390/app12010435

**AMA Style**

Zeng S, Kuang C, Yu W.
Evaluation of Real-Time Kinematic Positioning and Deformation Monitoring Using Xiaomi Mi 8 Smartphone. *Applied Sciences*. 2022; 12(1):435.
https://doi.org/10.3390/app12010435

**Chicago/Turabian Style**

Zeng, Shulin, Cuilin Kuang, and Wenkun Yu.
2022. "Evaluation of Real-Time Kinematic Positioning and Deformation Monitoring Using Xiaomi Mi 8 Smartphone" *Applied Sciences* 12, no. 1: 435.
https://doi.org/10.3390/app12010435