基于Elman神经网络的可见光室内定位算法研究

Research on Visible Light Indoor Localization Algorithm Based on Elman Neural Network

近年来,室内定位算法吸引了大量的关注和研究。为了改善现有定位算法的复杂度以及精确度等问题,提出了一种先利用Elman神经网络进行室内位置预测,使用加权K近邻算法(WKNN)对预测结果进行修正的可见光室内定位算法。该算法应用在由单LED灯作为发射器,4个水平光电探测器(PD)构成接收器的室内定位系统中。4个水平光电探测器分别位于接收器的4个角,待测位置位于接收器的中心。通过两个Elman神经网络分别预测待测点的横坐标和纵坐标来确定待测点的初步位置,找出定位误差大于神经网络预测平均误差的待测点,用加权K近邻算法进行修正来确定待测点的精确位置,将修正后的精确位置更新到整体待测点的位置中。仿真结果表明,在3.6 m×3.6 m×3 m的室内环境下,本研究算法的平均定位误差为7.13 cm,平均定位时间为0.24 s。

In recent years, indoor localization algorithms have attracted a great deal of attention and research interest. For the improvement of the complexity as well as the accuracy of existing localization algorithms, this paper proposes a visible light indoor localization algorithm that first uses Elman neural networks for indoor localization prediction and then uses the weighted K-nearest neighbor(WKNN) algorithm to correct the prediction results. The algorithm is applied in an indoor localization system with a single LED as a transmitter and four horizontal photoelectric detectors(PDs) as receivers. The four horizontal PDs are located at the four corners of the receiver and the position to be measured is located at the center of the receiver. The initial position of the point to be measured is first determined by predicting the horizontal and vertical coordinates of the point by two Elman neural networks. Then the point to be measured with a positioning error greater than the average error predicted by the neural network prediction is identified and corrected with the WKNN algorithm to determine the exact position of the point to be measured, and the corrected position is updated into the overall position of the point to be measured. The simulation results show that the average positioning error of this algorithm is 7.13 cm and the average positioning time is 0.24 s in an indoor environment of 3.6 m×3.6 m×3 m.