## 2 Related Work  
Indoor localization using UWB measurement has been studied in the past and in recent times based on TOA [10], time difference of arrival (TDOA) [11], phase of arrival (PoA) [12], angle of arrival (AOA) [13], joint TOA and AOA [14] and UWB localization algorithms have also been studied [15; 16]. In this section, we discuss different UWB localization systems that leverage the advantages of deep learning networks.  
The idea of machine learning in the UWB indoor localization research area was proposed in Reference [17] and this proposed system combines grid-search-based kernel support vector machine and principle component analysis (PCA). The results from Reference [17] show that the proposed approach reduces the localization error and improves the computational efficiency when compared to k-nearest neighbor (kNN), back propagation neural network (BPNN) and support vector machine (SVM) based methods. An SVM-based localization approach which classify the UWB NLOS conditions for indoor localization is presented in Reference [18]. The SVM model in Reference [18] reached 92% average identification accuracy for NLOS conditions and improved the UWB position accuracy in multipath conditions. To improve the localization accuracy of the UWB system during NLOS conditions, A two stage SVM classification for UWB LOS/NLOS channel conditions is discussed in Reference [19]. The system from Reference [19] shows 93.7% efficiency for identifying the LOS/NLOS channel conditions. However, selecting the adequate kernel and the parameter optimization are the major challenges faced by the SVM based localization systems. A logistic regression-based localization approach is an alternative way to improve the UWB localization performance for the non-line-of-sight (NLOS) condition. The regression model presented in Reference [20] addressed the NLOS detection error mitigation of differential time of arrival (TDoA) topologies and suppresses the NLOS error up to 80%. In Reference [21], the authors also introduced another regression-based localization method for identifying the LOS conditions. The PCA, kNN, SVM and logistic regression approaches gives better performance for UWB localization, however, the multipath effects, signal interference, sensitive to channel inconsistency and computational complexity of the models add localization errors to the UWB system. To extract the correct ToA during multipath scenarios, a signal processing method that uses deep learning to estimate the absolute UWB tag position directly from the raw channel impulse response (CIR) data is proposed in Reference [22]. The localization system explained in Reference [22] uses convolutional neural network (CNN) and achieved 17.3 cm mean absolute localization error for NLOS conditions. However, the overfitting & underfitting, parameter tuning and processing time of data from CNN models give challenges to the UWB localization. Most recent works on deep learning for UWB localization are explained in References [23; 24; 25]. The discussed localization systems from References [23; 24; 25] effectively minimize the ranging errors and computational burden and achieve excellent localization performance with much lower network complexity for UWB indoor localization.  
The UWB localization systems discussed in the related works addressed localization accuracy challenges and proposed a number of systems to improve indoor position accuracy. However, the UWB system still needs improvement in terms localization accuracy. Therefore, it is necessary to propose a new localization approach for UWB based indoor localization. To reduce the localization error, this paper proposes an LSTM based localization approach based on the simulation environment. The simulation results indicate that the proposed approach gives reasonable position accuracy for indoor localization and reduces the UWB localization errors.