Improving FTM Ranging Accuracy Based on DNN for UAV Localization

Recently, for indoor personal drones, there have been a number of challenges in localizing users, such as how to accurately identify their location and identification. This article proposes a distance measurement method for indoor unmanned aerial vehicle (UAV) localization combining fine-time measurement (FTM) ranging equipment and the deep neural network (DNN) model. Specifically, we can calculate the distance between the UAV and user by measuring the signal round-trip time. However, the indoor ranging accuracy of the FTM protocol is inevitably affected by the multipath and nonline-of-sight (NLOS). Besides, we prove that the FTM ranging under multipath is related to either the distance between the transceiver pair or the length of the reflected path relative to the direct one. Moreover, we also study the relationship between the FTM ranging error and the response of the multipath channel. Furthermore, we design an FTM error calibration model based on physical-layer (PHY) information by using the DNN model, termed DeepF, which can not only automatically distinguish environmental characteristics but also estimate the length of propagation paths of a signal. The designed DeepF can adopt the DNN model to extract the time domain information of channel state information (CSI) and learn the nonlinear mapping between delay, power, and mean error from signal features. Finally, we use a trained model to calibrate the FTM error and predict the user location. Experimental results show DeepF significantly improves the ability of indoor UAVs to localize users.