Indoor Localization Using Channel State Information With Regression Artificial Neural Networks

In this paper, the Channel State Information (CSI) is used to locate mobile stations in an indoor environment. The novelty of our technique is to use multiple packets of CSI for each location without feature extraction to provide a reach fingerprint. Two different mapping algorithms are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are fed to a multilayer perceptron (MLP) as input features and the learned artificial neural network (ANN) is used as a pattern-matching algorithm in order to predict a user's location. The second approach uses General Regression Neural Networks (GRNNs) from which, exploration is performed to find the best hidden-layer configuration and spread factors for Multilayer Perceptrons (MLPs) and General Regression Neural Networks (GRNNs), respectively. The novelty of this work partly stems from data expanding multiple CSI packets. The paper finally compares, the accuracy of our proposed method with previously reported state-of-the-art methods.