Enhanced Indoor Localization Based BLE Using Gaussian Process Regression and Improved Weighted kNN

Indoor positioning has attracted commercial developers and researchers in the last few decades. Global positioning system (GPS) cannot well localize in indoor environment. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. The received signal strength indicator (RSSI) fingerprinting plays a key role in the access point performance services. This paper deals with the issue of indoor localization based RSSI fingerprint using only RSSI vectors without any prior knowledge of the pose. We proposed to use machine learning (ML) combined with modified kNN algorithm to enhance the real-time performance and propose method to detect uncertainty of estimated pose. The mentioned ML algorithm is Gaussian Process Regression (GPR). In the online phase of our system, GPR gives a prediction for the location of the pose using RSSI vector. This prediction helps kNN algorithm therefore limits the searching region leading to reduce the computational cost. An analysis on the distribution of k nearest points is also presented which aims to evaluate the confidence of the estimated pose for extracting a list of trustable points. Furthermore, we also present an extrapolation process using this trust-list to have an optimized trajectory. The accuracy and timing analysis of our proposal was realized on a challenging BBIL dataset which contains very noisy RSSI signals due to a fast moving of the object. The experimental results show that our system exhibits a much better performance than traditional kNN or WkNN algorithms. The RMSE of our optimal trajectory is 1.78 m in the room with a dimension of 10 m $\times$ 25 m, which is competitive in comparison to other methods where initial pose is known.