Feature-Based Deep LSTM Network for Indoor Localization Using UWB Measurements

Abstract

Indoor localization using ultra-wideband (UWB) measurements is an effective localization approach when the localization system exists in non-line of sight (NLOS) conditions from the indoor experiment area. In UWB-based indoor localization, the system estimates the user's distance information using anchor-tag communication. The user's distance information in the UWB system is an influencing factor to determine localization performance. A deep learning-based localization system uses the raw distance information for model training and testing and the model predicts the user's current positions. Recently developed deep learning-based UWB localization approaches achieve the best localization results when compared to conventional approaches. However, when the deep learning models use raw distance information, the system lacks sufficient features for training and this is reflected in the model's performance. To solve this problem, we propose a feature-based localization approach for UWB localization. The proposed approach uses deep long short-term memory (DLSTM) network for training and testing. Using extracted features from the user's distance information gives a better model performance than raw distance data and the DLSTM network is capable of encoding temporal dependencies and learn high-level representation from the extracted feature data. The simulation results show that the proposed feature-based DLSTM localization system achieved a 5cm mean localization error as compared to conventional UWB localization approaches.