Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain

TL;DR

This paper introduces an optimal calibration scheme and a weighted least squares algorithm for RSS-based visible light positioning, significantly improving localization accuracy by calibrating LED tilt and gain, outperforming existing methods.

Contribution

It proposes a novel calibration and localization approach that optimally calibrates LED tilt and gain, achieving higher accuracy than machine learning and traditional techniques.

Findings

Achieves 58% and 74% improvements over GPs in 50th and 99th percentiles.

Reduces multilateration errors from 7.4 cm to 3.2 cm (50th percentile).

Validates effectiveness through simulations and real-world data.

Abstract

This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often overlooked impact of light emitting diode (LED) tilt. By optimally calibrating LED tilt and gain, we significantly enhance VLP localization accuracy. Our algorithm outperforms both machine learning Gaussian processes (GPs) and traditional multilateration techniques. Against GPs, it achieves improvements of 58% and 74% in the 50th and 99th percentiles, respectively. When compared to multilateration, it reduces the 50th percentile error from 7.4 cm to 3.2 cm and the 99th percentile error from 25.7 cm to 11 cm. We introduce a low-complexity estimator for tilt and gain that meets the Cramer-Rao lower bound (CRLB) for the mean squared error (MSE), emphasizing its precision and efficiency. Further, we elaborate on optimal calibration measurement placement and refine the observation model to include residual calibration errors, thereby improving localization performance. The weighted LS algorithm's effectiveness is validated through simulations and real-world data, consistently outperforming GPs and multilateration, across various training set sizes and reducing outlier errors. Our findings underscore the critical role of LED tilt calibration in advancing VLP system accuracy and contribute to a more precise model for indoor positioning technologies.