We put forward and experimentally demonstrate a second order machine-learning (ML) based visible-light-positioning (VLP) system using simple linear interpolation algorithm to reduce the training samples required in the ML algorithm. Algorithms of the second order regression ML model using 2,430 training samples; and using the reduced training samples of 570 with and without the proposed linear interpolation are compared and discussed. We can observe that the positioning accuracy of using training samples of 570 with the proposed interpolation can have similar performance when compared with using 2,430 training samples. The training samples are reduced by ∼76.5%. Here, off-the-shelf LED lamps and low bandwidth electrical and optical components are employed; and the system is cost-effective. Good quality on-off keying (OOK) identifier (ID) signals are retrieved after frequency down-conversion from 20 kHz, 40 kHz and 60 kHz without and with optical background noises respectively.

中文翻译：

---

使用线性插值减少基于回归的可见光定位系统的训练样本

我们提出并实验证明了一种基于二阶机器学习 (ML) 的可见光定位 (VLP) 系统，该系统使用简单的线性插值算法来减少 ML 算法所需的训练样本。使用 2,430 个训练样本的二阶回归 ML 模型的算法；并比较和讨论了使用和不使用建议的线性插值的 570 个减少的训练样本。我们可以观察到，与使用 2,430 个训练样本相比，使用 570 个训练样本和建议的插值的定位精度可以具有相似的性能。训练样本减少了约 76.5%。在这里，使用现成的 LED 灯和低带宽电气和光学组件；并且该系统具有成本效益。