Improved Wi-Fi Indoor Positioning Based on Particle Swarm Optimization

Abstract

Indoor positioning methods based on the received signal strength indication (RSSI) ranging technology are sensitive to various environmental noises, which cause positioning errors. An improved Wi-Fi indoor positioning method using an improved unscented Kalman filter and the particle swarm optimization (PSO) is proposed to reduce ranging error and improve positioning accuracy. The received signals are preprocessed by the improved unscented Kalman filter algorithm and then the improved PSO algorithm is used to optimize position calculation results. To demonstrate the utility of the proposed algorithm, simulations and experiments were performed for estimating the position of objects. Simulating results indicate that the mean error of the proposed algorithm is reduced by 31.87% in comparison with that of the unlicensed Kalman filter method. Experimental results show that the mean error of the proposed algorithm is reduced by 26.72% in comparison with that of the unlicensed Kalman filter method. Therefore, the proposed algorithm can effectively reduce the positioning error and improve the positioning accuracy. In the actual indoor positioning, it could get better positioning results.