Infrastructure-less indoor localization using light fingerprints
Infrastructure-less indoor localization using light fingerprints
复制标题
使用光指纹进行无基础设施的室内定位
DOI：
10.1109/icassp.2017.7953307
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Naveen Goela
中科院分区：
文献类型：
--
作者：
Shahab Hamidi;Kent Lyons;Naveen Goela
提出了一种基于高频光信号指纹的无基础设施室内定位系统。与其他调制光的系统相比，所提出的系统通过从训练样本中学习来区分光。由于紧凑型荧光灯（CFL）和发光二极管（LED）灯泡的电子元件略有不同，即使在同一品牌和型号内，每个灯泡发出的光信号也与其他灯泡略有不同。光信号通过快速精确的模数转换器（ADC）以高达1兆采样/秒的速度进行数字化、分段，并使用快速傅里叶变换（FFT）映射到频域。基于FFT的频谱特征被过滤、归一化，并用作监督机器学习算法的训练数据。提供了两种不同复杂度的分类器的结果：（1）k-最近邻（KNN）分类器;（2）卷积神经网络（CNN）分类器。设计了一个室内定位的硬件系统，分析了分类器的性能。在某些限制条件下，结果表明，灯泡可以识别高精度没有特殊的基础设施的调制。识别灯泡意味着与识别其相关联的位置同义。
An infrastructure-less indoor localization system is proposed based on fingerprints of light signals acquired at high frequencies. In contrast to other systems that modulate lights, the proposed system distinguishes lights by learning from training samples. Due to slight differences in the electronic components used in the construction of compact fluorescent light (CFL) and light emitting diode (LED) bulbs, the optical signals emitted by each light bulb have slight differences with other light bulbs even within the same brand and model. Light signals are digitized with a fast and accurate analog-to-digital converter (ADC) at up to 1 mega-samples/second, segmented, and mapped into the frequency domain using the Fast Fourier Transform (FFT). Spectral features based on the FFT are filtered, normalized, and used as training data for supervised machine learning algorithms. Results are provided for two classifiers of varying complexity: (1) A k-Nearest Neighbor (KNN) classifier; (2) A Convolutional Neural Net (CNN) classifier. A hardware system for indoor localization was designed to analyze the performance of the classifiers. Under certain restrictions, results show that light bulbs may be identified with high accuracy without special infrastructure for modulation. Identifying a light bulb is meant to be synonymous with identifying its associated location.