Infrastructure-less indoor localization using light fingerprints

Abstract

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.