Visible Light Communications Based Indoor Positioning via Compressed Sensing

TL;DR

This paper introduces a novel indoor positioning method using visible light communication and compressed sensing, enabling accurate location estimation by detecting nearby LEDs with sparse signal recovery algorithms.

Contribution

It proposes a compressed sensing-based approach for LED signal separation and position estimation in indoor VLC systems, improving accuracy in large-scale environments.

Findings

Achieves positioning accuracy close to theoretical lower bounds.

Effective in large-scale indoor open-plan office scenarios.

Utilizes sparse recovery algorithms for LED detection.

Abstract

This paper presents an approach for visible light communication-based indoor positioning using compressed sensing. We consider a large number of light emitting diodes (LEDs) simultaneously transmitting their positional information and a user device equipped with a photo-diode. By casting the LED signal separation problem into an equivalent compressed sensing framework, the user device is able to detect the set of nearby LEDs using sparse signal recovery algorithms. From this set, and using proximity method, position estimation is proposed based on the concept that if signal separation is possible, then overlapping light beam regions lead to decrease in positioning error due to increase in the number of reference points. The proposed method is evaluated in a LED-illuminated large-scale indoor open-plan office space scenario. The positioning accuracy is compared against the positioning error lower bound of the proximity method, for various system parameters.