Generative Adversarial Networks based Data Recovery for Indoor Localization

To localize objects in an indoor environment, several methods are used such as trilateration and fingerprinting. These methods are based on the Received Signal Strength Indicator (RSSI), which is very sensitive to propagation factors indoor due to the existence of different static and dynamic obstacles. The use of RSSI causes the problem of missing data because the RSSI transmitted by an access point is not correctly received by the object sensors. To deal with the problem of missing data, researchers propose several completion methods, nevertheless no definitive solution has been brought. Therefore, we propose in this paper to use Generative Adversarial Networks for data recovery in order to be used efficiently for objects localization. The results based on the simulation data show an improvement of the localization accuracy compared to the classical methods. The simulations were performed with 10% and 50% of missing data and improved of 29.19% and 37.51% respectively the localization accuracy.