A UWB/Bluetooth Fusion Algorithm for Indoor Localization

Target positioning is difficult in indoor environment where Global Positioning System (GPS) always fails to locate for lack of signals. Therefore indoor localization system based on the Wireless Sensor Networks (WSNs) attracted considerable attention due to the growing need for Location Based Service (LBS). Both Ultra-Wideband (UWB) and Bluetooth are widely applied in indoor localization. However, the applicability of them is limited for their high price or poor accuracy. In order to improve the positioning accuracy, stability and cost reduction, a combined indoor localization scheme and a fusion algorithm using characteristics of two positioning methods are proposed. Bayesian Inference and geometry relationships are applied to get the objective function and constrain, and Particle Swarm Optimization (PSO) is employed to obtain the estimated position. Simulation results show that the new algorithm can improve the positioning accuracy and reduce the economic price compared with the traditional Least Square (LS) positioning algorithm based on distance.