Smartphones, being one of the most ubiquitous sensors in daily life, have the capability to receive Global Navigation Satellite System (GNSS) signals, thereby enabling them to provide location-based services (LBSs) for mass-market users. Spatial information is one of the vital components in intelligent transportation and internet of things applications, and transportation-related applications like lane-level navigation are among the most frequently used smartphone LBSs. Considering the high noise level of smartphone GNSS measurements in such applications, there is a risk of selecting a reference satellite with measurement outliers in relative positioning technology, which would therefore decrease positioning accuracy and reliability. To address this issue, this paper proposes a resilient reference satellite configuration in smartphone relative positioning, where two reference satellites are selected per frequency for each constellation, accompanied by an automatic switching strategy between single and dual-reference satellite configurations. The proposed method extends the observation equations with a second reference satellite, and is validated with 18 datasets collected in driving environments, and both theoretical analysis and positioning results demonstrate that the dual-reference satellite configuration outperforms conventional single-reference satellite strategies except in extremely harsh environments. When applying the resilient switch, the percentage of horizontal positioning errors within 4 meters is largely improved. Moreover, the 68th percentile horizontal positioning errors are reduced by ~3 decimeters compared to single reference satellite method, and the percentages of positioning errors within 1.0 and 1.5 meters are improved by 8% and 9%, respectively, indicating a higher capability and great potential of providing lane-level navigation with the proposed resilient reference satellite configuration.

The advancement of Internet of Things (IoT) and autonomous driving applications has resulted in significantly increased demands for lane-level navigation using consumer-grade smartphones. However, the frequent occurrence of carrier phase cycle slips in smartphones severely degrade positioning accuracy, posing a major obstacle to achieving reliable lane-level navigation. While various cycle slip detection methods exist, their reliance on fixed and empirical thresholds often fails to adapt the diverse characteristics of different smartphone brands, which can lead to either excessive false detections or missed cycle slips, thereby destabilizing the positioning solution. To address this gap, this study first quantifies the impact of small cycle slips on the sub-meter positioning accuracy of smartphones by inserting several simulated cycle slips in realistic dynamic driving scenarios. The results indicate that the impact of small cycle slips on lane-level navigation is at the centimeter-level, the reason might be that the small cycle slips are accommodated by the poor-precision smartphone phase measurements and ambiguity estimates. Consequently, only a negligible effect is brought on overall navigation performance, motivating the development of loose detection thresholds. Furthermore, utilizing 211 vehicular dynamic datasets from the Google Smartphone Decimeter Challenge (GSDC), this study analyzes the distributions of the difference between Doppler and time-differenced carrier phase (D-TDCP) test values for different smartphone brands, including Xiaomi Mi8, Pixel, and Samsung. The analysis reveals that the distributions exhibit significant brand-specific differences and are strongly correlated with the signal-to-noise ratio (SNR). Based on these insights, an adaptive, SNR-fitted threshold scheme for smartphone Real-Time Kinematic (RTK) is proposed, dynamically adjusting detection thresholds for per smartphone brand and frequency. Validation on 103 independent test datasets demonstrates that, compared with conventional fixed and tight threshold methods, the proposed adaptive scheme reduces the 68thand 95thpercentile horizontal positioning errors by 17 cm and 36 cm, respectively. Moreover, it increases the proportion of positioning errors within 1 meter from 47% to 55%, inferring stable and reliable lane-level positioning capability.

The ubiquity of smartphones catalyzes myriad smartphone-based Internet of Things (IoT) applications, amongst which smartphone positioning which utilizes global navigation satellite system (GNSS) observations to provide spatial information plays a crucial role. However, noisy smartphone GNSS measurements prevent decimeter-level positioning performance in user environments. Recovering the integer property of GNSS carrier phase measurement ambiguities shows great potential in achieving high-accuracy positioning solutions. However, inaccurate ambiguity estimates and short signal wavelengths are the main barriers to successful ambiguity resolution (AR). Therefore, a partial AR with wide-lane (WL) ambiguities and an automatic ambiguity hold strategy is proposed. Simulated results show that, for single-epoch WL AR, even with one WL ambiguity correctly fixed, the positioning solution can be improved by 3 cm. With actual static and kinematic datasets, the proposed algorithm is evaluated and validated. Static results show an improvement of 83% in horizontal position when the WL AR approach is applied, and positioning accuracies can reach 6.8, 2.9, and 11.5 cm in the E, N, and U direction components, respectively. For kinematic data collected in highly variable realistic driving environments, the time series of positioning errors of WL AR solutions exhibit less variation than float solutions. And with fixed WL ambiguities, solutions can be improved to varying degrees, ranging from several centimeters to up to 8 dm depending on the environment. The largest improvement of 8 dm is observed for 95th percentile horizontal positioning errors under a suburban environment.

The last decade has seen substantial advancements in Internet of Things (IoT)-based transportation and smart city networks, fueling the growth of Global Navigation Satellite System (GNSS) industries and GNSS-enabled smartphones that deliver real-time, precise location-based services for mass-market applications. However, achieving decimeter-level smartphone positioning with GNSS processing techniques, such as precise point positioning (PPP) with real-time corrections in urban environments remains challenging due to the noisy and unstable nature of smartphone GNSS measurements. Key issues include low signal strength, high multipath effects, frequent cycle slips, and phase discontinuities, all of which degrade PPP accuracy and extend convergence times. To address these challenges, this study introduces a two-step clock bias preprocessing method to reduce Galileo High Accuracy Service outliers and biases. Additionally, an innovative iterative PPP algorithm integrated with a moving window approach is proposed to mitigate cycle slip false alarms and preserve ambiguity estimation continuity under difficult GNSS signal reception conditions. Validated through extensive vehicle experiments across eight datasets in diverse multipath environments, the proposed method demonstrates significant positioning accuracy improvements with four-constellation support. Results show a 95th percentile error and overall rms of 1.8 and 1.2 m, respectively, in horizontal positioning, with submeter lateral rms (0.8 m) and 99% lane-determination success rate in realistic driving scenarios. These findings indicate the potential of smartphone-based real-time PPP in enabling lane-level navigation, paving the way for next-generation IoT-integrated location services.