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.

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect. The CE module outputs coarse on-grid angle estimation (respectively observed from the BS and RIS) to the localization module, which returns the fine off-grid angle estimation to improve CE. Particularly, with RIS, CDL can obtain user's location via line intersection, and a polar-domain gradient descent (PGD) algorithm at the base station is proposed to achieve the off-grid angle estimation with super-resolution accuracy. Additionally, to further reduce the sensing overhead, we propose a partial dictionary-based localization scheme, which is decoupled from CE, where RIS is served as an anchor to lock the user on the hyperbola according to time difference of arrival and the user's off-grid location can be obtained by using the proposed PGD algorithm. Simulation results demonstrate the superiority of the two proposed localization schemes and the proposed CE scheme over state-of-the-art baseline approaches.

It is well-known that the Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) /Vision integration has been increasingly used for safety-critical applications like self-driving cars. However, the performance of the GNSS/INS/Vision integration degrades due to unknown faults. Therefore, it is essential to develop integrity monitoring algorithms for the integrated navigation system. A new protection level (PL) formula to ensure the integrity of the state estimation of the GNSS/INS/Vision integration is proposed, which can be calculated by the relation between the filter estimation error and faults. An integrity risk allocation tree is developed for each sensor fault hypothesis including the nominal hypothesis, and the specific PL is given in the case of GNSS, INS and visual measurement faults. In addition, the performance of the proposed PL is comprehensively evaluated. A field vehicular test was conducted to evaluate the performance of the integrity monitoring algorithm for the GNSS/INS/Vision integration. The results demonstrate that the proposed PL for the GNSS/INS/Vision integration can fit the position error against six different fault modes.

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.

Smartphone-based pedestrian dead reckoning (PDR) has been widely used indoors for continuous localization. However, the specific tracking solutions under different modes are vulnerable to mode transition and thus degrading performance. The robustness of pedestrian navigation may be weakened due to the mix of smartphone motions and walking patterns. Due to this challenge, most existing PDR methods assume that the smartphone is carried in a certain pose, such as handheld horizontally, swinging, calling, and pocketed, ignoring the short period but negative transition impact on tracking, which limits its flexibility when applying in the Internet of Things (IoT) services. To achieve a fully flexible PDR (i.e., FlexPDR), this article enhances the robustness and smoothness of pedestrian tracking during the transition between several phone poses and regular motion modes for the first time. We propose a Bayesian-based real-time multimode recognition method that does not require any posterior information, together with a time-series decomposition approach for adaptively tracking scheme-switching. The proposed FlexPDR system achieved a real-time smartphone indoor positioning with a high position accuracy of 98.11% on the specific situation that mixed mode-switching happens, which outperformed other state-of-the-art methods.