Foot-mounted inertial pedestrian positioning system (FIPPS) is increasingly important in the Internet of Things (IoT) and smart cities because of the passive, anti-interference, and fully automatic advantages. However, FIPPS faces the problem of unknown initial positions and accumulative errors in practical applications, which makes the positioning trajectory inaccurate and unable to be matched in a well-defined coordinate system. A seamless indoor–outdoor inertial pedestrian positioning method based on smartphone precise point positioning (PPP)/3-D map/barometer augmentation is proposed to solve this problem. The main contributions include the following: 1) a 3-D-mapping-aided PPP (3DMA PPP) method is proposed to detect and eliminate non-line-of-sight (NLOS) signals to provide high-precision initial coordinates for FIPPS, which enables positioning trajectories to be matched in the unified WGS-84 coordinate system; 2) the adaptive zero-velocity update (ZUPT) approach based on neuro-fuzzy inference is used to precisely identify the stance phases of various gait patterns to suppress position and velocity error accumulation; 3) a barometer and 3-D building model-based height constraint algorithm is applied to further improve the height estimation of the FIPPS; and 4) an Android application named FYTECH is developed for data transmission between the smartphone and FIPPS and supports the online display of pedestrian positioning trajectories. In a seamless outdoor–indoor experiment with multiple motion patterns, including walking, running, elevators and stairs, we demonstrate that the proposed method can achieve RMS errors better than 1.17 and 1.32 m in the horizontal and vertical directions, respectively. The performance indicates that our method can conveniently fuse multisource sensor information, such as foot wearables, smartphones, and 3-D maps to greatly enhance pedestrians’ seamless indoor–outdoor navigation experience.

Navigating to a destination using smartphones has recently become a primary way in people’s daily lives. Positioning is the prerequisite condition before efficient path planning can be achieved. In urban environments, people can self-localize with the aid of the global navigation satellite system (GNSS). However, last-mile navigation is still a challenge for pedestrians as current indoor positioning techniques cannot provide an accurate position. For the past few years, many researchers in academia and industry have thrown themselves into smartphone-based indoor positioning. Various solutions have been proposed to enable accurate and reliable position solutions. Pedestrian dead reckoning (PDR) is a popular positioning method as it is self-contained and outperforms inertial navigation. Also, other information from sensor data features and infrastructures, such as radio frequency (RF), can ensure the positioning service. At present, there are various indoor positioning reviews available to summarize current techniques. However, they focus primarily on separate positioning methods rather than integration. This paper aims to comprehensively review PDR-centric indoor positioning using smartphones. It covers the basic concept of PDR and its integration with other information, examining both infrastructure-free and infrastructure-dependent approaches. Furthermore, this paper outlines key considerations for conducting a comprehensive comparative analysis of algorithm. Finally, the challenges and future research trends are given. This paper can help readers understand the features of different PDR-centric integrations. It also contributes to the design of more robust solutions to satisfy the ever-increasing demand for indoor positioning.

The Ionosphere Working Group of the International GNSS Service (IGS) has been a reliable source of global ionospheric maps (GIMs) since 1998. Modeling of the global ionospheric total electron content (TEC) is performed daily by several Ionosphere Associate Analysis Centers (IAACs). Four IAACs (CODE, ESA, CAS and WHU) use the spherical harmonic (SH) expansion as their primary method for modeling GIMs. The IAACs generally solve a normal equation to obtain the SH coefficients and Differential Code Biases (DCBs) of satellites and receivers by traditional least-squares estimation (LSE) without any prior knowledge. In this contribution, an improved method is proposed and developed for global ionospheric modeling based on utilizing prior knowledge. Prior values of SH coefficients and DCBs of satellites and receivers, as well as the variance factor and covariance matrix, could be obtained from the ionospheric modeling on the previous day. The parameters can subsequently be updated through GNSS measurements to achieve higher accuracy. Comparisons are carried out between WHU products based either on priori information or original LSE and IGS final products, other IAAC products, and JASON data for the year 2014. The results indicate that there is improved consistency between WHU GIMs and IGS final GIMs, other IAAC products, and JASON data, particularly in comparison with ESA and UPC products, with the probabilities of achieving better consistency with these products exceeding 95%. Moreover, WHU-produced DCBs of satellites also have slightly improved consistency with IGS final GIMs and IAAC products.

Modern railway track health monitoring requires high accuracy measurements to ensure comfort and safety. Although Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integration has been extended to track geometry measurements to improve the work efficiency, it has been questioned due to its positioning accuracy at the centimeter or millimeter level. We propose the relative spatial accuracy based on the accuracy requirement of track health monitoring. A requirement assessment of the spatial relative accuracy is conducted for shortwave track irregularity measurements based on evaluation indicators and relative accuracy calculations. The threshold values of the relative spatial accuracy that satisfy the constraints of shortwave track irregularity measurements are derived. Motion-constrained GNSS/INS integration is performed to improve the navigation accuracy considering the dynamic characteristics of the track geometry measurement trolley. The results of field tests show that the mean square error and the Allan deviation of the relative position errors of motion-constrained GNSS/INS integration are smaller than 0.67 mm and 0.16 mm, respectively, which indicates that this approach meets the accuracy requirements of shortwave track irregularities, especially vertical irregularities. This work can provide support for the application of GNSS/INS systems in track irregularity measurement.