基于RSSR融合RNGO-Elman神经网络的室内可见光定位

Indoor Visible-light Localization Based on Received Signal Strength Ratio using Fused RNGO-Elman Neural Network

摘要:

针对动态环境下基于接收信号强度的传统可见光定位方法定位精度低、稳定性差等问题,提出一种基于接收信号强度比的改进北方苍鹰算法(NGO)优化Elman神经网络(RNGO-Elman)的室内可见光定位系统。提出选择一个辅助参考点,将待测参考点与辅助参考点的接收信号强度比值和接收机的真实位置作为训练集数据,建立不受动态环境影响的指纹数据库。针对NGO算法收敛速度慢、容易陷入局部最优等问题,利用折射反向学习策略初始化种群,增加种群多样性,引入非线性权重因子来加快收敛速度,避免陷入局部最优。使用优化后的NGO算法来优化Elman神经网络的初始权值和阈值,构建RNGO-Elman动态定位预测模型。仿真结果表明,在4m×4m×3m的实验空间下,优化后的RNGO-Elman定位模型平均定位误差为1.34cm,定位精度相较于Elman定位算法、NGO-Elman定位算法分别提高了82%,21%。在LED发射功率波动时,基于RSSR的RNGO-Elman定位误差为1.29cm,1.38cm。所提可见光定位方法具有定位精度高、定位性能稳定等优点。

Abstract:

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 4m×4m×3m, the optimized RNGO-Elman localization model had an average localization error of 1.34cm, 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 1.38cm. The proposed visible light positioning method has the advantages of high positioning accuracy and stable positioning performance.