RFID/ In-vehicle Sensors-Integrated Vehicle Positioning Strategy Utilising LSSVM and Federated UKF in a Tunnel

Abstract

This paper proposes a Radio Frequency Identification (RFID)/ in-vehicle sensors fusion strategy for vehicle positioning in completely Global Positioning System (GPS)-denied environments such as tunnels. The strategy employs a two-step approach, namely, the calculation of the distances between the RFID tags and the reader, and then the global fusion estimation of vehicle position. First, a Least Square Support Vector Machine (LSSVM) algorithm is developed to obtain distance. Next a novel Federated Unscented Kalman Filter (FUKF) is designed to realise the global fusion. The decentralised federated filter is adopted to combine the data from RFID and in-vehicle sensors, and the UKF is employed to design a local filter since it has better ability to deal with a nonlinear problem than an Extended Kalman Filter (EKF). Due to the optimised layout of RFID tags and the application of the decentralised filter, the number of tags is reduced. Finally, the feasibility and effectiveness of the proposed strategy are evaluated through experiments.