The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

Smartphone with multiple sensors integration has been widely used for navigation. The Inertial Measurement Unit (IMU) embedded in smartphones has been widely used for pedestrian navigation for counting steps. However, it is a challenge to measure the accurate velocity of the vehicle from the smartphone-embedded IMU data with a high noise level. Thus, current vehicle navigation with a smartphone relies substantially on the Global Navigation Satellite System (GNSS), which provides unreliable positions in urban dense areas due to the blockage and the reflection of GNSS signals. In this study, we propose a smartphone-based positioning method to improve vehicle positioning performance continuously in GNSS-degraded areas through the improvement of IMU velocity estimation. A convolutional neural network–Gated Recurrent Unit (CNN-GRU) combined deep learning odometry network, termed DeepOdo, is proposed to estimate the velocity of the vehicle with the IMU and barometer data as the input, rather than the traditional integral of the IMU measurements. Raw sensor data is utilized to boost the robustness. Labels of the DeepOdo are obtained from the integrated GNSS/IMU/Barometer solutions in the smartphone which significantly simplifies the dataset collection. In GNSS-denied areas, IMU, Barometer, and DeepOdo are integrated to provide accurate navigation solutions for the vehicle. Results of the proposed method show 73.14% and 98.33% improvements in horizontal and vertical directions respectively, compared with the Non-Holonomic Constraints (NHC) aided IMU. Finally, the DeepOdo network is deployed in Android smartphones to demonstrate that the proposed solution can work properly on the mobile platform.