# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
###### 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. indoor position; robust filter; integrated system; BLE; PDR +
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applied to indoor position service applications with high real-time performance. Fortunately, Bluetooth-based techniques are another good choice to position due to the increased popularity of smartphones along with the development of the Internet of Things. Low cost, power-saving, and ease of deployment without affecting the infrastructures of buildings can avoid the shortcoming of Wi-Fi scanning [17]. Most importantly, the principle of the BLE positioning method is the same as that of Wi-Fi. Some of the current Wi-Fi positioning algorithms can be directly used for BLE positioning [18]. The fingerprint-based method of BLE is one of the popular approaches for the indoor position which can offer many advantages for the realizable position accuracy and is infrastructure-free. However, the radio signals of BLE can be affected by the multipath effect and device heterogeneity [19] which cause the signal to change during offline acquisition and online position, resulting in the error in the positioning process. In short, all of these indoor position techniques mentioned above have both advantages and disadvantages. It is the focus of researchers to propose a fusion of indoor position methods to keep a balance among accuracy, coverage, cost, and complexity. Common fusion position methods include multimodal fingerprinting, triangulation-based fusion, and pedestrian dead reckoning (PDR)-based fusion [20]. The PDR-based fusion method which combines PDR with wireless localization methods is widely used in the literature. PDR is a self-positioning method that provides a relatively high accuracy position estimation based on the smartphone's built-in sensors, but it suffers from the drift problem, resulting in huge cumulative error for long-time positioning [21]. By contrast, wireless positioning method such as BLE fingerprint position can obtain absolute position without cumulative error but has poor accuracy position estimation. Fusing PDR with wireless localization methods which are often known as Bayes filter, Kalman Filter (KF), Extended Kalman filter (EKF), or particle filter (PF) can make up for both methods' shortcomings to provide high accuracy and stable position service. The PF has good performance in solving nonlinear problems but suffers from a high computational load. In our research, the fusion EKF algorithm was chosen to combine PDR with the BLE fingerprint position method in the real-time integrated system.