Data-efficient Gaussian process regression for accurate visible light positioning

In the field of indoor localization systems, Received Signal Strength (RSS) based Visible Light Positioning (VLP) has gained increased attention due to the dual functionality of lighting and localization. Previously geometrical models have been used to determine the position of a mobile entity, however these are unsuited when dealing with tilted surfaces and non-Lambertian sources. For this reason, machine learning techniques like Multi Layer Perceptrons (MLPs) have been considered recently. In this work, Gaussian Processes (GPs) are introduced in the context of RSS-based VLP, since they have proven to work well when using small, noisy datasets for different applications. Their performance is evaluated using both simulated data with a small transmitter tilt tolerance and measurements. It is demonstrated that the GP model outperforms both the multilateration approach and the MLP approach for the simulations and measurements data

Experimental evaluation of machine learning methods for robust received signal strength-based visible light positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.status: publishe

Bayesian active learning for received signal strength-based visible light positioning

Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength (RSS) and fixed LED-based light transmitters. Classical VLP approaches use lateration or angulation based on a wireless propagation model to obtain location estimations. However, previous work has shown that machine learning models such as Gaussian processes (GP) achieve better performance and are more robust in general, particularly in presence of non-ideal environmental conditions. As a downside, Machine Learning (ML) models require a large collection of RSS samples, which can be time-consuming to acquire. In this work, a sampling scheme based on active learning (AL) is proposed to automate the vehicle motion and to accelerate the data collection. The scheme is tested on experimental data from a RSS-based VLP setup and compared with different settings to a simple random sampling