Recurrent Model for Wireless Indoor Tracking and Positioning Recovering Using Generative Networks

Indoor person tracking attracts a considerable effort from the research community as it allows to perform Human Behaviour Analysis tasks, where wireless technologies play a key role. However, complex signal propagation effects in indoor environments are the main issue to face when performing accurate indoor positioning and tracking. The advances in machine and deep learning models, applied to improve the estimation of the position captured by wireless sensors, can provide a more precise tracking and positioning, an open field for research which has been used to improve the prior art. In this paper, a novel framework for Adaptive Indoor Tracking using Recurrent models, in combination with Generative networks for new data generation (recovery), is presented (RecTrack-GAN). Firstly, a Received Signal Strength Indicator RSSI Fingerprinting database is collected. Secondly, a Recurrent Neural Network (RNN) takes as input the RSSI parameters collected by a Wireless Sensor Network (WSN) and estimates of both orientation and velocity using devices equipped with Inertial Measurement Unit (IMU) sensors, and learns to model the human movement based on these parameters. Thirdly, a Conditional Generative Adversarial Network (CGAN) is used to perform data recovering when no measurements are received and to update the Fingerprinting database taking into account the day time. The experiments performed showed that RecTrack-GAN improves accuracy performance and reduces error deviation for tracking up to 15% compared to the prior art in the literature.