Neural Networks for Indoor Localization based on Electric Field Sensing

In this paper, we will demonstrate a novel approach using artificial neural networks to enhance signal processing for indoor localization based on electric field measurement systems Up to this point, there exist a variety of approaches to localize persons by using wearables, optical sensors, acoustic methods and by using Smart Floors. All capacitive approaches use, to the best of our knowledge, analytic signal processing techniques to calculate the position of a user. While analytic methods can be more transparent in their functionality, they often come with a variety of drawbacks such as delay times, the inability to compensate defect sensor inputs or missing accuracy. We will demonstrate machine learning approaches especially made for capacitive systems resolving these challenges. To train these models, we propose a data labeling system for person localization and the resulting dataset for the supervised machine learning approaches. Our findings show that the novel approach based on artificial neural networks with a time convolutional neural network (TCNN) architecture reduces the Euclidean error by 40% (34.8cm Euclidean error) in respect to the presented analytical approach (57.3cm Euclidean error). This means a more precise determination of the user position of 22.5cm centimeter on average.