## 1 Introduction  
Recent advancements in indoor localization technologies [1] give accurate position results when the GPS signals are unable to detect user position in an indoor scenario. The most popular building dependent localization technology is the ultra-wideband (UWB) localization system. In UWB localization, multiple anchors are placed in the experiment area and the user uses a UWB tag to communicate with anchors. The anchors and the tag communicate through UWB signals and the time of arrival (TOA) [2] of UWB signals are used to estimate the distance of the user from anchors. The position accuracy of a UWB system depends on the UWB signal shapes, TOA of UWB signals and user distance estimation from the anchors. For an accurate user position estimation in UWB systems, it is necessary to estimate the user distance with a minimum error. In UWB localization, the localization algorithms use the user distance information from the TOA-distance model. The trilateration algorithm is the most common localization algorithm used in UWB localization. However, the position accuracy of the trilateration algorithm depends on the user distance from the anchors [3]. The multipath effects,channel conditions between anchors and tag, and the UWB signal shapes affect the distance estimation accuracy. This leads to a high position error in the UWB localization systems. To reduce the user position errors in the UWB system, we propose a deep learning approach to UWB localization. The proposed model consists of a deep neural network which takes as input the distance information from the TOA-distance model and predicts the user positions. The proposed model uses a two layer long short-term memory (LSTM) network [4] which identifies the accurate user positions. As compared to neural network (NN), the LSTM has feedback loop which serves as a memory and gives better performance for time series analysis. The flexibility in controlling the input data from the LSTM model gives a better localization performance than other models. The constant error backpropagation within memory cells results in LSTM model can able to bridge very long-time lags for UWB localization. The unique features of LSTM model show that it is the best choice for our localization purpose. Deep learning for UWB localization has been investigated through several approaches to improve localization performance. Many researchers proposed different deep learning approaches such as convolutional neural networks (CNN) for UWB transmitter-receiver distance estimation [5], non-line of sight (NLOS) channel classification using channel impulse response (CIR) information [6; 7] and the neural network for UWB fingerprint-based localization [8]. However, to the best of our knowledge, there is no comparative analysis of hyperparameter tuning for LSTM based UWB localization. Therefore, it is necessary to investigate the best hyperparameters for LSTM based UWB localization and this paper analyses the performance of LSTM deep learning networks in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes and timesteps. The best parameters for LSTM networks are identified through several simulation results and finally, we compare the proposed model prediction results with conventional UWB localization approaches such as linearized least square estimation (LLSE), fingerprint estimation (FPE), maximum likelihood estimation (MLE) and weighted centroid estimation (WCE) algorithms [9]. The performance of the LSTM model also analyzed by mean localization error with a different number of LSTM layers, training and testing time of UWB data in LSTM model and the mean localization error with a different number of anchors. Through extensive simulations, we demonstrated the superior performance of the proposed LSTM model. The following are the main contributions to this paper:  
* We simulated a UWB system with three anchors and one tag, and generated Gaussian pulses as UWB signals.