Extreme Learning Machine Ensemble for CSI based Device-free Indoor Localization

Abstract

A bstract-Device-free localization is a new and developing technology which estimates an object's locations without requiring it to equip any devices. Channel State Information (CSI), containing more fine-grained information than Received Signal Strength Indication (RSSI), is a natural candidate for localization application and has been studied in many works. Extreme Learning Machine (ELM) is a fast and robust algorithm, but it has only one hidden layer, which limits its capacity. One of the most popular ways of improving accuracy is to use multiple different models to obtain better predictive performance. In this paper, we propose a device-free localization approach using an ensemble of ELMs in which each ELM has the same number of hidden nodes. The proposed approach models the localization task as a regression problem. First, we leverage a modified driver to collect CSI and extract phase information. The Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the phase features. After that, the processed features are fed into an ensemble of ELMs to output their respective predictions. The final prediction is an average combination of them. We conducted experiments in a typical indoor environment to verify its performance, and the results demonstrated the effectiveness of our approach and of CSI.