Hybrid Indoor Positioning Method of BLE and PDR Based on Adaptive Feedback EKF With Low BLE Deployment Density

Abstract

Location-based service (LBS) has gradually penetrated into all aspects of human life. The current indoor positioning algorithms include triangulation, least squares, fingerprint, maximum likelihood estimation, and so on. These positioning algorithms have requirements for the deployment density of the access point (AP). Since the AP deployment density is directly related to the equipment cost, the AP-based positioning method has a high-cost problem that cannot be ignored. Furthermore, the positioning accuracy of traditional algorithms will sharply decline in the positioning field with low AP deployment density. In order to solve the positioning problem in application scenarios with low AP deployment density, this article proposes a hybrid indoor positioning method of Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) based on the adaptive feedback extended Kalman filter (AFEKF). The AFEKF algorithm uses an adaptive feedback EKF to deeply fuse the position of the BLE, the range measurement, and the result of PDR localization, making full use of the information of the BLE (the position, the orientation, and the range measurement) instead of only the range measurement. The range measurement is deeply fed back to the estimated position, thus adaptively adjusting the position estimate at the next moment, making the position estimate more accurate and the algorithm more robust. Experiments in large underground parking lots show that the AFEKF algorithm proposed in this article can achieve high-precision and low-cost indoor positioning, and effectively solve the positioning problem in open scenes with low AP deployment density.