Efficient Wi-Fi signal strength maps using sparse Gaussian process models
Mostafa M. Sakr, Naser El‐Sheimy · 2017
This objective of this paper is to propose and evaluate a new algorithm to increase the computation and storage efficiency and to reduce the bandwidth requirements of the Wi-Fi received signal strength indicator (RSSI) maps based on Gaussian Process (GP) models. GP models are non-parametric models that estimate the likelihood function of the target variable, in this case the Wi-Fi RSSI values, conditioned on a set of training data. This paper introduces the Parametric Grid Sparse GP (PGSGP) algorithm, to improve the efficiency of using GP maps. The PGSGP reduces the complexity of evaluating the likelihood function, by reducing the number of points in the training dataset, without significant loss of the mapping or positioning accuracy. This is achieved by finding a set of pseudo-inputs arranged over a parametric grid, then optimizing the corresponding target values and the GP model hyperparameters.