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.

High-precision GNSS positioning plays a crucial role in intelligent transportation systems, and leveraging artificial intelligence can enable the development of reliable context-aware models that enhance context-adaptive GNSS algorithms. However, the complexity of contexts and the variability of features limit the performance of GNSS-based context-aware models. Therefore, this study aims to construct a context-aware model in kinematic vehicle-mounted environments. Firstly, due to the lack of publicly available datasets, a kinematic vehicle-mounted dataset comprising over 40,000 data samples is constructed. This dataset includes five contexts including open area, urban canyon, boulevard, under viaduct, and tunnel. Meanwhile, efficient feature selection methods are applied, namely SHAP (Shapley Additive exPlanations) and SFFS (Sequential Forward Floating Selection), to obtain effective data features. Secondly, considering the temporal correlation of GNSS signals, eight alternative models (SVM, MLP, CNN-b, CNN-r, CNN-i, ML-LSTM, ML-RNN, ML-GRU) are designed and trained. Context probability is introduced to indicate context transition area. Comparative studies show that the ML-LSTM model exhibits the best performance, with an an accuracy of 92.72% and a mean average precision (mAP) of 92.94% in context-separated areas, and with an accuracy of 87.49% and a mAP of 89.21% in context-continuous areas. Furthermore, to address the performance degradation when deploying the model on different receiver types, this study explores model-based transfer learning. Four transfer approaches, namely direct transfer, fully connected layer transfer, partial layer transfer, and all layer transfer, are applied. The all layer transfer approach demonstrates the best performance, exhibiting a 18.34% improvement compared to the non-transfer approach.

Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively.

Because of the demand for seamless indoor and outdoor positioning of mass-market applications, several simple and efficient pedestrian dead reckoning (PDR) systems have been introduced. However, the performance of the PDR system would significantly decrease with the increase of recursion time, due to the influence of sensors and model error. This paper discusses the overview of the pedestrian navigation system and the implementation of key techniques of PDR, then proposes an improved PDR system with precise displacement estimation based on time-differenced carrier phase (TDCP) technique. Using the derived position change and heading from TDCP as updating information, the bias of stride and heading of the modeled PDR system will be estimated through a sliding window. The field test results demonstrate that the errors of gyroscope heading and compass heading show different characteristics, and there is a linearly increased error of gyroscope heading. The max errors of accumulated displacement of TDCP are about 5.32 m, and using pure PDR after trained by TDCP, the max error values of short-term and long-term navigation are approximately 5.97 and 14.88 m, respectively. From the point view of cumulative probability, for the probability of 80%, the horizontal errors of accumulated displacement of TDCP is only 3.78 m, and values of short-term and long-term navigation reach to 4.15 m and 11.78 m, respectively. These results indicate that the proposed algorithm is stable and practical to the continuous pedestrian navigation during the intermittent global navigation satellite system observation condition.

In the era of intelligence, there is a huge demand for precise positioning in the fields such as automatic driving, assisted driving, and vehicles to everything. Due to the continuous and high-precision positioning capability, the global navigation satellite system (GNSS) has been an important foundation for large-scale location applications in open sky conditions. For land vehicle kinematic navigation, the GNSS positioning method based on Kalman filtering (KF) has also become the primary choice. However, the general state model based on velocity and acceleration in KF lacks mining and utilization of the physical laws of vehicle motion, resulting in the insufficient performance of GNSS KF positioning in urban conditions. Thus, we constructed a hybrid state model, where the vehicle kinematics are classified into non-linear and quasi-linear motions, and the corresponding velocity constraint (VC) and heading constraint (HC) models are constructed respectively. Then the reparameterization between VC-model and HC-model is derived through a strict formula, and the hybrid state model can switch adaptively by vehicle motion recognition. Field vehicle test data was collected with a low-cost GNSS receiver and processed by different methods. The test results of different observation conditions and different motions show that the hybrid state model has better positioning performance, and the root means square (RMS) errors in the occlusion condition are reduced by 15% compared with only the VC-model. Further, the test results using the public data of the Google Decimeter Challenge project also verify the effectiveness and practicability of the filtering model in vehicular positioning.