With the rapid development of the Internet of Things (IoT), indoor positioning method with characters of high accuracy, continuity, and reliability has become an urgent requirement. Although the Global Satellite Navigation System (GnSS) can provide that information in open-sky conditions, it still cannot work in GNSS-denied environments. To counteract the drawbacks of GNSS and conventional low-accuracy indoor technologies, this paper presents a credible indoor positioning system with integrating Ultra-Wide-Band (UWB) measurements and low-cost Inertial Navigation System (INS) tightly. Based on such a system, the impacts of Non-line-of-sight (NLOS) errors, the distribution of UWB base stations, the number of UWB base stations, and the rate of UWB data on UWB/INS tight integration are evaluated comprehensively by analyzing three sets of experimental data and several groups of simulated data. Results demonstrate that (1) UWB/INS integration upgrades UWB positioning accuracy significantly, with decimeter-level positioning accuracy in the horizontal component and meter-level accuracy in the vertical direction while using the UWB/INS tight integration model. (2) The influences of the geometry structure of the UWB base station on the performance of UWB and the UWB/INS loose integration are obvious, and such an influence on the performance of the UWB/INS tight integration is slight. (3) The positioning accuracy of UWB/INS tight integration increases gradually along with the rate of UWB data. (4) The presented method shows visible repression of the influence of NLOS errors on UWB/INS tight integration.

Smartphone-based Pedestrian Dead Reckoning (PDR) is widely used in Internet of Things (IoT) applications. However, the accuracy and reliability of PDR could be affected by the users’ environments significantly. To upgrade PDR’s performance, we present an enhanced PDR algorithm, in which pedestrian motion constraints, smartphone sensors, and a combined step detection method are integrated with PDR to provide continuous, accurate, and reliable position solutions. In such a method, measurements of tri-axis accelerometers, tri-axis gyroscopes, tri-axis magnetometers, a barometer, and a Global Navigation Satellite System (GNSS) chip from HUAWEI Mate30Pro are integrated by an Extended Kalman Filter (EKF). Pedestrian motions like motionless and linear motion form constraints to upgrade the performance of the multi-sensor enhanced PDR. Results based on a set of experimental data demonstrated that the proposed PDR could provide positioning accuracy in terms of Root Mean Square Error (RMSE) within 1.5 m. Compared to PDR-only, the position enhancements on vertical and horizontal from a barometer, tri-axis accelerometers, tri-axis magnetometers, GNSS, and motion constraints are visible with improvement percentages of more than 98.9%, which makes the position solutions of the presented PDR much more reliable than the conventional PDR.

Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the IGG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%.

The integration of global navigation satellite system (GNSS) single-frequency (SF) real-time kinematics (RTKs) and inertial navigation system (INS) has the advantages of low-cost and low-power consumption compared to the multiple-frequency GNSS RTK/INS integration system. However, due to the vulnerability of GNSS signal reception, the application of the GNSS SF-RTK/INS integration is limited in complex environments. To improve the positioning accuracy of SF-RTK/INS integration in GNSS-blocked environments, we present a low-cost tight integration system based on BDS/GPS SF-RTK, a low-cost inertial measurement unit (IMU), and a monocular camera. In such a system, a multi-state constraint Kalman filter (MSCKF) is adopted to integrate the single-frequency pseudo-range, phase-carrier, inertial measurements, and visual data tightly. A wheel robot dataset collected under satellite signal-blocked conditions is used to evaluate its performance in terms of position, attitude, and run time, respectively. Results illustrated that the presented model can provide higher position accuracy compared to those provided by the RTK/INS tight integration system and visual-inertial tight integration system. Moreover, the average running time presents the potential of the presented method in real-time applications.

Real-time kinematic (RTK) based on single-frequency observation is still widely used in many fields due to the characteristics of low-cost and low-power consumption. However, the positioning performance of single-frequency RTK in terms of accuracy, stability, and continuity would be significantly degraded during the harsh satellite environments. To improve the performance, this contribution presented a model of multisensor and analytical observations augmented the single-frequency RTK tightly based on a modified Psi-angle state model. In such a model, the single frequency observations of the new signal of third generation BDS (BDS-3) B2b are tightly integrated with inertial measurements, odometer data, dual-antenna attitude, and nonholonomic constraint (NHC). To evaluate the presented model, the typical navigation performance and the ambiguity resolution (AR) performance are analyzed based on a set of vehicle-borne data. Results illustrated that the inertial navigation system (INS) would bring about 13.5%, 16.2%, and 12.3% position enhancements to the BDS-3 B2b RTK mode. Such improvements could be up to 15.9%, 16.2%, and 25.2% while adding the NHC and odometer data. Besides, NHC and odometer also upgrade the attitude accuracy visibly in pitch and heading directions, with enhancements of about 16.9% and 62.9%. In contrast, augmentations from the dual-antenna attitude are mainly presented in terms of heading angle with about 29.5% compared to the RTK/INS/Odometer/NHC tight integration mode. Besides, the convergence time of yaw angle is visibly enhanced while using the odometer/NHC, the dual-antenna heading, or the two together. Moreover, the AR performance could also be improved while using the presented model. Due to the enhancements in position and attitude brought by different sensors, the ADOP performance would be improved to varying degrees. Besides, the fixed rate and reliability of AR could also be enhanced.