Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can't provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.

The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub.

We propose a fast satellite selection algorithm for multi-constellation, which is based on both Newton's identities for Geometric dilution of precision (GDOP) fast computation and optimal satellite geometric distribution for less cycle participation. An effective closed-form formula is proposed for GDOP approximation, avoiding conventional matrix inversion and complexity. We reduce computational cycles with auxiliary optimal satellite geometric distribution, which improves the efficiency of realtime positioning under the combined action of both. Simulation results indicate that we can save more than 44% and 99% computational complexity when selecting 7 satellites from 10 and 30 visible satellites respectively. The GDOP of proposed method is very close to optimal result, better than that of Quasi-Optimal algorithm.

The detection and repair of the cycle slip is a key step for high precision navigation and positioning in indoor environments. Different methods have been developed to detect and repair cycle slips for carrier phase processing. However, most approaches are designed to eliminate the effects of the ionosphere in an outdoor environment, and many of them use pseudorange (code) information that is no longer suitable for indoor multipath environments. In this paper, a method based on the geometry-free combination without the pseudorange data is proposed to detect and fix cycle slips. A ground-based navigation system is built for data collection. Unlike the traditional dual-frequency cycle slip detection method, the Beidou B1, GPS L1 carrier phase combination is used instead of the B1, B2, or L1, L2 carrier phase combination, Ublox is used for data collecting. For fixing the cycle slips quickly, an improved adaptive Particle Swarm Optimization (PSO) algorithm is employed. We compared the performance of the new method with the existing two methods using simulated data in different conditions. The results show that the proposed method has better performance than other methods.

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.