# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
###### Abstract
It has been widely used in building emergency management [15], occupancy detection [16], smart grid applications [17], and smart energy management [18]. That is also why we select BLE to combine with PDR in this paper. An integrated system combining BLE and PDR is a feasible solution for realizing real-time and long-term indoor positioning of a smartphone with relatively high precision [3; 10; 19; 20]. Due to the short-term high-precision features of the PDR method, occasional abnormal BLE signals caused by a non-line-of-sight (NLOS) environment or interference in similar frequency bands can be detected and effectively eliminated. Meanwhile, the positions obtained from normal signals with the BLE localization can be used to correct and inhibit the error accumulation of the PDR method. In this paper, we present a real-time indoor localization method for smartphones based on the combination of BLE and PDR positioning in the particle filter framework. Particle filtering has been proven an effective framework that fuses the RSS-based and PDR positioning methods [8; 21; 22; 23]. The proposed fusion method can effectively reduce the cumulative error of PDR and mitigate the influence of the RSS fluctuations and thus provide more accurate and reliable positioning for smartphones. The contributions of this paper are listed in the following:  
* To realize the PDR method, first, peak detection is used for step detection based on the measured acceleration; second, an empirical nonlinear step length estimation model is adopted based on the measured acceleration in a single stride; third, the heading direction is estimated by using the complimentary filter to combine the measurements from magnetometer, gyroscope, and accelerometer. * For BLE-based localization, a range-free weighted centroid algorithm is proposed instead of commonly-used range-based trilateration or fingerprint-based method. This range-free method has low computation complexity and is proven to be efficient, especially when the BLE anchor tags are densely deployed. * The particle filter framework is adopted to fuse the positioning results obtained by PDR and BLE-based methods. As the PDR method cannot determine the initial position and suffers from error accumulation over time, and the BLE-based method cannot provide continuous and stable positioning results due to signal fluctuation, the combination of the two methods under the particle filter framework is able to overcome the shortcomings of both sides and provides more stable and accurate positioning results. * We have implemented the proposed positioning method on the smartphone and performed indoor positioning experiments on a floor of the campus building. Experimental results indicate that our proposed fused positioning method offers more accurate and stable performance in the long run compared with pure PDR or BLE-based positioning. The rest of the paper is organized as follows. Section 2 presents related work. Section 3 describes the nuts and bolts of our proposed method, including the overall architecture, the PDR method, the range-free BLE-based method, and the use of a particle filter framework. Performance evaluation by experiments is discussed in Section 4. Finally, Section 5 concludes this paper.