论文标题
使用人工神经网络基于光通道脉冲响应的本地化
Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network
论文作者
论文摘要
可见的光定位有可能在室内环境中产生亚中心精度，但是常规接收的信号强度（RSS）基于基于的定位算法无法实现这一目标，因为它们的性能从光学多径反射中降低。但是，由于光学无线通道的通常静态和可预测的性质，光学接收信号的这一部分是确定性的。在本文中，使用人工神经网络（ANN）研究了光通道脉冲响应（OCIR）的性能（OCIR）本地化，以将OCIR的嵌入式特征映射到用户设备的位置。数值结果表明，基于OCIR的本地化仅使用两个光电播放器作为锚点，优于两个数量级的常规RSS技术。 ANN技术可以在各种场景中利用多径功能，从仅使用DC值到依靠高分辨率的时间采样，从而导致次级中心精度。
Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy.
