A Hybrid Algorithm Based on WiFi for Robust and Effective Indoor Positioning

Abstract

Indoor positioning based on the Wireless Fidelity (WiFi) protocol and the Pedestrian Dead Reckoning (PDR) approach is widely exploited because of the existing WiFi infrastructure in buildings and the advancement of built-in smartphone sensors. In this work, a hybrid algorithm that combines WiFi fingerprinting and PDR to both exploit their advantages as well as limiting the impact of their disadvantages is proposed. Specifically, to build a probability map from noisy Received Signal Strength (RSS), a Gaussian Process (GP) regression is deployed to estimate and construct the RSS fingerprints with incomplete data. Mean and variance of generated points are used to estimate WiFi fingerprinting position by K-nearest weights from the probability of visible RSS measurements of the online phase. In addition, a particle filter is applied to fuse PDR and WiFi fingerprinting by using the information from RSS, inertial sensors and features of indoor maps. To demonstrate the potential of the proposed framework, two case studies are considered. In the first case, a comparison is made between GP regression with K-Nearest Neighbours (KNN) method to show the improvement with a sparse input data set. In the second case, the proposed framework is compared to both the fingerprinting approach as well as the PDR algorithm. The results show significant improvements from our proposed framework. The average positioning accuracy of our proposed system can be lower than 1.2 m, which was reduced by 48% and 70% compared with the WiFi fingerprinting and the PDR method, respectively.