Abstract
Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.
Highlights
With the rapid development of technology and people’s increasing demands for a better life, various applications based on location-based service (LBS) provide a great convenience for people’s life
Stable position service in indoor environments, many types of indoor positioning technologies such as wireless fidelity (Wi-Fi) [1,2,3], Bluetooth low energy (BLE) beacons [4,5], radio frequency identification (RFID) [6,7], ultrasonic [8], infrared [9], ultra-wideband (UWB) [10,11], pseudolite [12,13], computer vision [14,15] had been proposed by experts and scholars
The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running
Summary
With the rapid development of technology and people’s increasing demands for a better life, various applications based on location-based service (LBS) provide a great convenience for people’s life. According to the latest google android development document [16], it is specified that the scanning frequency of Wi-Fi is limited no more than four times in two minutes after the Android Oreo system It is difficult for the Wi-Fi-based positioning method to be applied to indoor position service applications with high real-time performance. The fusion EKF algorithm was chosen to combine PDR with the BLE fingerprint position method in the real-time integrated system. To meet the demand of real-time position and considering the computational load of the smartphone, we adopt the EKF method to solve the nonlinear fusion problem to combine PDR with the BLE fingerprint position method to provide the real-time position service. The discussion of the paper and the conclusions are presented in Section 5 and Section 6
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