There are many applications for indoor personnel tracking and localization using infrared thermal sensors, such as health monitoring and indoor security surveillance. This method is the best suitable for tagless localization of human bodies, adapting to different users and different scenarios. Since Infrared sensors are more affordable than other sensors used to detect and track people and their activities, infrared sensors are currently widely used in the Internet of Things. This thesis evaluates the Long Short-Term Memory neural networks for predicting data from a low-resolution 16-pixel thermopile sensor data for indoor localization and tracking, improving robustness and reliability by adding unrelated noise to the sensor data.