A Robust Heading Estimation Solution for Smartphone Multisensor Integrated Indoor Positioning

As a part of Internet of Things applications, various smartphone-based indoor location services have considerable commercial value. It is largely agreed that the integration of multiple sensors is the preferable solution for improving the performance of smartphone indoor positioning thanks to the diversity of built-in sensors. However, heading error remains a challenge for smartphone indoor positioning, especially in complex indoor scenes. This paper therefore proposes a heading estimation solution to enhance the accuracy and reliability of smartphone indoor positioning. The EKF (extended Kalman filter)-based solution fuses smartphone built-in motion sensors, magnetometers, building map knowledge and fingerprinting coarse positions from Wi-Fi or Bluetooth. First, the context of pedestrian mobility and scene knowledge are inferred by combining these data. Then, a scene augmentation strategy and magnetic interference online detection method are applied to calibrate the gyro accumulation error and improve the heading estimation accuracy. Additionally, quasi-static and low-dynamic judgments and online calibration are used to mitigate gyro drift. The proposed solution is implemented on a smartphone device and validated in several experiments under natural pedestrian mobility and complex indoor scenarios. Experiments show that the accuracy of heading estimation is improved from 13.1∘ to 2.0∘. The improved heading estimation enhances the accuracy of smartphone indoor positioning from 3.57 m to 0.90 m. The proposed solution is applicable to real location-based service scenarios.