With the explosive development of pervasive computing and the Internet of Things (IoT), indoor positioning and navigation have attracted immense attention over recent years. Pedestrian dead reckoning (PDR) is a potential autonomous localization technology that obtains position estimation employing built-in sensors. However, most existing PDR methods assume that the smartphone is held horizontally and points to the walking direction. To solve reckoning errors caused by inconsistency of headings between walking heading and pointing of smartphone, we design an accurate and robust PDR method based on walking patterns, which is identified by multihead convolutional neural networks. In addition to adaptively adjust the threshold of step detection and select the most suitable step length model according to the results of walking pattern recognition, a novel heading estimation approach independent of device orientation is proposed. To mitigate accumulative errors, we proposed an online trajectory calibration method based on forward and backward magnetic fingerprint trajectory matching. We conduct extensive and well-designed experiments in typical scenarios, and the experimental results indicate that the 75th percentile localization accuracy of the three scenarios is 1.06, 1.08, and 1.22 m, respectively, using the commercial smartphone embedded sensor without any dedicated infrastructures or training data. Despite the intricate pedestrian locomotion, the proposed PDR method has great potential in pedestrian positioning.

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.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Simultaneous localization and mapping (SLAM) is currently a widely used technology for indoor positioning. Many studies have focused on the smartphone-based localization and navigation for its portability and feature-rich sensors. But due to the low-quality sensors and complex environments, current SLAM-based positioning technology using smartphones still poses great challenges, such as inherent cumulative global drift and potential divergence facing texture-less indoor regions. For the regular gait characteristics of pedestrians when naturally walking, the gait motion model can certainly provide effective observation of the pedestrian motion state. We propose a visual-inertial odometry (VIO) assisted by pedestrian gait information for smartphone-based indoor positioning. This work mainly builds two additional state constraints, pedestrian velocity, and step displacement, obtained by the pedestrian dead reckoning (PDR) algorithm for the visual-inertial tracking system. For each step, the corresponding residual term of step length and velocity constraints is constructed and added to the cost function for nonlinear sliding-window optimization. Furthermore, the step displacement is applied again in the four-degree-of-freedom (4-DOF) graph-based optimization to refine the trajectory. VIO system also assists the PDR algorithm in mode switching, to improve the accuracy of gait information by applying the adaptive step length formula. Field experiments were conducted, and the results indicate that with the aiding of the pedestrian gait information, the accuracy and robustness of the visual-inertial pedestrian tracking system using smartphones have been significantly improved. Compared with the state-of-the-art algorithm monocular visual-inertial navigation system (VINS-MONO), our method improves the accuracy by 54.6% on average in our field tests in challenging environments.

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

With the increasing complexity of electromagnetic environment and the continuous progress of electronic interference technology, Global Navigation Satellite System (GNSS) spoofing attacks have become a practical threat. As a result, the focus of GNSS improvement has shifted from improving accuracy to improving reliability. Combining an inertial navigation system (INS) is effective for spoofing detection and mitigation since INS can independently provide high frequency, high accuracy, multi-category navigation information within a period of time. This article focuses on analyzing the impact of spoofing attacks on GNSS/INS integration and explores potential anti-spoofing methods. A Beidou Navigation Satellite System (BDS) real-time kinematic (RTK)/INS tightly coupled model that involves spoofing measurements is established. The article describes an adaptively robust Kalman filter (ARKF)-based spoofing detection and mitigation method. The impact of spoofing attacks on the adaptively robust estimation is theoretically assessed. A combined spoofing-resistant strategy of ARKF and partial ambiguity resolution (PAR) is proposed to improve the ability of state estimation under GNSS spoofing. Semi-physical and semi-simulation experiments are carried out to verify our analysis and approach, which shows that compared with using extended Kalman filter (EKF), the BDS RTK/INS tightly coupled integration using ARKF can suppress the effect of spoofing attacks. However, its performance is degraded under ramp spoofing attacks and long-term step spoofing attacks. Additionally, employing a partial ambiguity resolution can reduce the effect of outliers on ambiguity resolution, improve the accuracy of state estimation under spoofing attacks, and further enhance the system’s robustness under GNSS spoofing.