Indoor Visible-light Localization Based on Received Signal Strength Ratio using Fused RNGO-Elman Neural Network
Aiming at the problems of low positioning accuracy and poor stability of traditional visible-light positioning methods based on the strength of the received signal in dynamic environments,this paper proposes an indoor visible-light positioning system using an improved northern goshawk optimization(NGO)algorithm fused with an optimized Elman neural network(RNGO-Elman)based on the received signal strength ratio(RSSR).This article proposes selecting an auxiliary reference point,using the RSSR of the test reference point to the auxiliary reference point and the true position of the receiver as the training set data to establish a fingerprint database that is not affected by a dynamic environment.Aiming at the problems of NGO algorithms such as slow convergence speed and tendency to fall into local optimums,the refractive reverse learning strategy was used to initialize the population,increase its diversity,and introduce nonlinear weighting factors to accelerate the convergence speed and avoid falling into local optimums.The improved NGO algorithm was used to optimize the initial weights and thresholds of the Elman neural network and construct the RNGO-Elman dynamic localization prediction model.The simulation results show that under an experimental space of 4 m×4 m× 3 m,the optimized RNGO-Elman localization model had an average localization error of 1.34 cm,and the localization accuracy was improved by 82％and 21％compared with the Elman localization algorithm and the NGO-Elman localization algorithm,respectively.When the LED emission power fluctuated,the positioning errors of the RNGO-Elman model based on the RSSR were 1.29 and 1.38 cm.The proposed visible light positioning method has the advantages of high positioning accuracy and stable positioning performance.
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