Instituto de Telecomunicações and Departamento de Electrónica, Telecomunicações e Informática, Universidade de Aveiro, 3810-193 Aveiro, Portugal.
Optical Communications Research Group, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Sensors (Basel). 2022 Apr 8;22(8):2879. doi: 10.3390/s22082879.
In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.
在本文中，我们研究了一种室内可见光定位（VLP）系统的设计方面，该系统通过考虑多径信道，使用人工神经网络（ANN）进行定位估计。以前的结果通常依赖于有限有效性的简单视线模型。本研究考虑了噪声的影响，作为不同设计方法之间比较的性能指标。考虑了三种不同的 ANN 算法，包括 Levenberg-Marquardt、贝叶斯正则化和比例共轭梯度算法，以最小化 VLP 系统中的定位误差（εp）。基于隐藏层神经元的数量、训练时期的数量和训练集的大小对 ANN 设计进行了优化。结果表明，在所有信噪比（SNR）值下，具有贝叶斯正则化的 ANN 都优于使用非线性最小二乘估计的传统接收信号强度（RSS）技术。此外，在内部区域（包括发射机的接收平面区域内），对于 SNR 分别为 10、20 和 30 dB 的情况，定位精度分别提高了 43%、55%和 50%。在外部区域（房间内剩余区域），对于 SNR 分别为 10、20 和 30 dB 的情况，定位精度分别提高了 57%、32%和 6%。此外，我们还分析了 ANN 中不同训练数据集大小的影响，结果表明，使用随机选择方案可以实现 SNR 为 30 dB 时最小 εp 为 2 cm。最后，即使 SNR 值较低，也可以观察到 εp 较低，即 SNR 分别为 30、20 和 10 dB 时，εp 值分别为 2、11 和 44 cm。
