A DW-SAPSO Localization Method based on Correctional RSSI Ranging Model

Abstract

Aiming at the problem of low localization accuracy caused by the ranging error generated by the RSSI ranging model with empirical parameters in different indoor scenarios, a simulated annealing-based particle swarm optimization localization algorithm with decreasing inertia weight (DW-SAPSO) is proposed. In the RSSI ranging phase, the parameters of ranging model are estimated and corrected by the difference between different reference points. In the localization phase, the nonlinear cost function is constructed according to the measurable quantity. Simultaneously the simulated annealing and decreasing inertia weight mechanism are introduced to the standard particle swarm optimization (PSO) localization algorithm, which effectively improves the algorithm’s global optimization ability and local search accuracy. Experimental results show that compared with an existing method and the PSO localization algorithm, the modified DW-SAPSO localization algorithm has better convergence performance and can improve the average localization accuracy of the latter by about 32.7%, which has some practical value.