Kalman Filtering-Aided Hybrid Indoor Positioning System With Fingerprinting And Multilateration

Abstract

In this work we resort to the Bluetooth Low Energy (BLE) beaconing mechanism to propose an hybrid indoor positioning system (H-IPS) that fuses both multilateration (MLT) and fingerprinting (FP) RSSI-based approaches. The aim is to estimate the localization of an indoor target node, which is assumed to follow a uniform motion model. We adopt Kalman Filtering (KF) to diminish the MLT and FP errors while performing a track-to-track fusion (TTF) of the two KF outputs to further improve the performance. Our results indicate that the proposed H-IPS improves the estimation accuracy when individually compared to the standalone FP scheme in up to 46% in the considered scenarios, while the standalone MLT is outperformed in approximately 54%. Moreover, we also provide some insights on the influence of parameters such as the FP grid size, number of access points (APs) and number of samples on the accuracy of the proposed scheme. Finally, we show that the probability that the distance error of the proposed H-IPS is lower than 2 m is 92%, while for the FP and MLT the same probability is 43% and 47%, respectively.