Adaptive Kalman Tracking for Indoor Visible Light Positioning

TL;DR

This paper introduces an adaptive Kalman filter approach for indoor visible light positioning that dynamically adjusts to changing access point availability, significantly improving localization accuracy in VLC networks.

Contribution

It proposes an adaptive Kalman filter that modifies parameters based on access point availability, enhancing indoor VLC user tracking accuracy.

Findings

Reduces localization RMSE by 30-50%

Improves tracking accuracy during access point transitions

Demonstrates effectiveness through simulation results

Abstract

Visible light communication (VLC) utilizes light-emitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of localization. In this paper, we study tracking a VLC user when the availability of VLC access point (AP) link changes over the user's route. We propose a localization method for a single available AP and use known estimation methods when a larger number of APs are available. Tracking mobile users with Kalman filter can increase the accuracy of the positioning, but the generic Kalman filter does not consider instant changes in the measurement method. In order to include this information in the position estimation, we implement an adaptive Kalman filter by modifying the filter parameters based on the availability of APs to the user. Simulation results show that the implemented method decreases the root-mean-square error (RMSE) of the localization down to 30%-50% of the original estimation.