Database Assisted Nonlinear Least Squares Algorithm for Visible Light Positioning in NLOS Environments

TL;DR

This paper introduces a database-assisted nonlinear least squares algorithm for indoor visible light positioning that effectively accounts for NLOS effects, improving localization accuracy and robustness.

Contribution

It combines fingerprinting and NLS techniques with a database to learn NLOS effects, enhancing indoor visible light positioning accuracy.

Findings

The proposed DA-NLS algorithm outperforms traditional fingerprinting and NLS methods.

It effectively models NLOS effects to improve localization robustness.

Experimental results demonstrate significant accuracy improvements.

Abstract

We propose an indoor localization algorithm for visible light systems by considering effects of non-line-of-sight (NLOS) propagation. The proposed algorithm, named database assisted nonlinear least squares (DA-NLS), utilizes ideas from both the classical NLS algorithm and the fingerprinting algorithm to achieve accurate and robust localization performance in NLOS environments. In particular, a database is used to learn NLOS effects, and then an NLS algorithm is employed to estimate the position. The performance of the proposed algorithm is compared against that of the fingerprinting and NLS algorithms.