A RSSI ranging algorithm based on GWO-BP neural network
Recently,the research on received signal strength indication(RSSI)based ranging has received a significant atten-tion,especially in the field of Internet of things and indoor positioning.Precise distance measurement is the basis for high-pre-cision positioning based on ranging algorithms,but the RSSI signal is highly fluctuating due to measurement noise and multi-path effects,which leads to a non-uniform mapping relationship between RSSI and the real physical distance in space.In order to enhance the mapping relationship between RSSI and real physical distance and improve the precision of RSSI ranging,this paper proposes a RSSI ranging algorithm based on GWO-BP neural network,which makes use of back propagation(BP)neural network and gray wolf optimization(GWO)algorithm.GWO algorithm has faster convergence and greater stability than parti-cle swarm optimization(PSO),genetic algorithm(GA),differential evolution(DE),evolutionary programming(EP)and evolution strategy(ES).Furthermore,in this paper,the results of the experiments conducted in two different environments by collecting real data through the developed smartphone software show that:the root mean square error(RMSE)of the path loss model(PLM)based ranging were 2.218,2.059 m,the RMSE of the traditional BP neural network ranging algorithm were 1.541,1.551 m,and the RMSE of the GA algorithm-based optimized BP neural network ranging algorithm were 1.269,1.201 m,respectively,and the RMSE of the GWO-BP neural network ranging algorithm proposed in this paper were 1.054,0.833 m,respectively.The results indicate that the RSSI ranging algorithm proposed in this paper has higher ranging precision and better robustness.
path loss modeloptimization algorithmBP neural networkRSSI rangingindoor positioning
万方数据
维普
