An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning

Yang, Jianwei; Gao, Yuting; Ding, Wei; Liu, Fei; Gao, Yang

doi:10.3390/s23115292

Cited by 5 publications

(6 citation statements)

References 51 publications

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“…Figure 15a compares experimental distributions of the TDDFC metrics from the static GNSS receivers and the flight test data. Therefore, using the well-known carrier phase measurement noise model derived from static ground receivers in [36] may lead to a significant number of false alarms in detecting cycle slips. The effect of tight thresholds not only reduces the average number of carrier phase measurements, but frequent exclusions and re-introduction of satellites into the KF lead to poor convergence performance overall.…”

Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

“…The effect of tight thresholds not only reduces the average number of carrier phase measurements, but frequent exclusions and re-introduction of satellites into the KF lead to poor convergence performance overall. Figure 15b shows the minimum, maximum, and average number of carrier phase measurements remaining after the proposed cycle slip detectors with threshold in [36] and from Table 3, respectively. It can be clearly seen that with the TDDFC threshold in [36], a large amount of GNSS data from moving UAVs would be declared as a cycle-slipped carrier phase and excluded accordingly.…”

Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

“…Figure 15b shows the minimum, maximum, and average number of carrier phase measurements remaining after the proposed cycle slip detectors with threshold in [36] and from Table 3, respectively. It can be clearly seen that with the TDDFC threshold in [36], a large amount of GNSS data from moving UAVs would be declared as a cycle-slipped carrier phase and excluded accordingly.…”

Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

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Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Shin,

Lee,

Kim

2024

Machines

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Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%.

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Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

Section: Performance Improvement Via Proposed Cycle Slip Detection St...mentioning

confidence: 99%

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Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Shin,

Lee,

Kim

2024

Machines

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“…The work in [ 8 ] proposed an improved method from traditional Ambiguity Resolution (AR) to achieve centimeter-level smartphone tracking based on a search-and-shrink procedure coupled with the methods of the multi-epoch double-differenced residual. The tests were developed using a Xiaomi Mi 8 in static mode to evaluate smartphone AR efficiency and a Google Pixel 5 (Google LLC, Mountain View, CA, USA) in kinematic mode to verify the improvement in smartphone positioning performance.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Xiaomi Mi 8 GNSS Antenna Performance

Zabala Haro,

Martín Furones,

Anquela Julián

et al. 2024

Sensors

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The interest in precise point positioning techniques using smartphones increased with the launch of the world’s first dual-frequency L1/L5 GNSS smartphone, Xiaomi Mi 8. The smartphone GNSS antenna is low-cost, sensitive to multipath, and limited by physical space and design. The main purpose of this work is to determine the mechanical location and antenna performance in terms of radiation pattern in an anechoic chamber using a Vector Network Analyzer (VNA) and robotic positioning platform by varying the elevation and azimuth angles between the transmitter and smartphone GNSS antennas; the power received and satellite visibility are developed in an outdoor scenario. The results show a Planar Inverted-F Antenna with an omnidirectional radiation pattern without gain. The L1/E1/B1 and L5/E5a/B2a GNSS antennas are physically located at the top face of the screen, with dimensions of 48 × 17 mm and 60 × 13 mm, respectively. With the screen with line-of-sight toward the sky, L5 satellites have a better signal–noise ratio (SNR), unlike the back side, which loses 99% of the data in the PPP solution. Under multipath scenarios, the L1 GNSS smartphone antenna works with 25% less power than the GPS user segment recommendation, showing high sensitivity to track weak signals.

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“…However, the convergence time typically spans between 10 to 30 min, a duration primarily dependent on the surrounding environment [ 3 , 4 , 5 ]. In a real-time kinematics (RTK) mode, decimeter or even centimeter positioning accuracy can be realized in static open spaces under ideal conditions [ 6 , 7 , 8 , 9 ]. However, dynamic positioning in urban environments presents challenges due to observational noise and multipath influences, making ambiguity difficult to fix and reducing the average positioning accuracy to meter levels, thereby yielding suboptimal results [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning

Deng,

Wang,

Wei

et al. 2024

Sensors

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In order to overcome the limitations of traditional stochastic models for smartphones, we introduce a double-difference code pseudorange residual (DDPR)-dependent stochastic model based on an optimal satellite subset, with the goal of mitigating the constraints imposed by the quality of GNSS observations in smartphones on the accuracy and reliability of phone-based GNSS positioning. In our methodology, the satellite selection process involved considering the integrated carrier-to-noise density ratio (C/N0) index of both the reference station and the smartphone, enabling us to construct a satellite subset characterized by superior observation quality. Furthermore, by leveraging the optimal subset of satellites and incorporating the C/N0-dependent stochastic model, we could determine the approximate location of the terminal through pseudorange differential positioning. Subsequently, we estimated the DDPRs for all satellites and utilized these values as prior information to build a stochastic model of the observations. Our findings indicate that in occluded environments, the DDPR-dependent stochastic model significantly enhances positioning accuracy for both the Huawei Mate40 and P40 terminals compared to the C/N0-dependent model. Numerically, the improvements in the north (N), east (E), and up (U) directions were approximately 30%, 32%, and 34% for the Mate40, and 26%, 33%, and 24% for the P40 terminal. This suggests that the proposed DDPR-dependent stochastic model effectively identifies and mitigates large gross error signals caused by multipath and non-line-of-sight (NLOS) signals, thereby assigning lower weights to these problematic observations and ultimately enhancing positioning accuracy. Moreover, the weighting method involves minimal computations and is straightforward to implement, making it particularly suitable for GNSS positioning applications on smartphones in complex urban environments.

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An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning

Cited by 5 publications

References 51 publications

Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles

Comprehensive Analysis of Xiaomi Mi 8 GNSS Antenna Performance

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning

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