# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 2 Related Work  
Research using neural networks is conducted in various fields, and it was recently applied to the field of indoor positioning. Although fewer studies were conducted using deep learning than the existing indoor positioning techniques, it can be seen from the results of research using neural networks that there are still many applications in the indoor positioning field. In Reference [16], instead of locating a mobile user's position one at a time as in the case of conventional methods, their RNN solution aims at trajectory positioning. Moreover, the proposed method considers the correlation among the received signal strength indicator (RSSI) measurements in a trajectory. However, as the data are collected using a robot, there may be a slight difference in the results obtained using data collected by a dedicated user. [PERSON] et al. [17] developed an algorithm based on deep belief networks, and they conducted evaluations using a combination of Wi-Fi and Bluetooth signals. Although they achieved a mean accuracy of 0.52 m, the time and battery consumption increased owing to the use of both signals. [PERSON] et al. [18] filled in missing RSS values using regression, and then applied linear discriminant analysis to reduce features. Before applying a deep neural network (DNN) for localizing Wi-Fi users, they appended five basic service set identifications (BSSIDs) having the strongest RSS values with a reduced RSS vector. [PERSON] et al. [19] used a deep learning architecture for regression and a support vector machine for classification to output the estimated location directly from the measured fingerprint. [PERSON] et al. [20] proposed a Bluetooth low energy (BLE) localization system using a denoising autoencoder to build a fingerprint database in three-dimensional (3D) space. However, if the target area becomes very wide, such as a shopping mall, many BLE devices must be installed; thus, positioning using only BLE is limited in practice. [PERSON] et al. [21] proposed a deep learning scheme based on channel state information to obtain more fine-grained information on the wireless channel than RSS-based methods, such as the amplitude and phase of each subcarrier from each antenna. They used deep learning to train all the weights of a deep network as fingerprints in the offline training phase. In the online localization, they used a stochastic method based on the radial basis function to obtain the localization result. However, the experiments in their study were conducted in a very small space. Several researchers also conducted indoor localization using convolutional neural networks (CNNs). [PERSON] et al. [22] focused on the pose regression problem, and they introduced a deep neural network architecture for RGB-Depth images and a training method for dual-stream CNNs. They discussed different depth image encoding methods and proposed a novel encoding method for indoor relocalization. [PERSON] et al. [23] presented a localization method that uses a hybrid wireless fingerprint based on a CNN. The proposed fingerprint method combines the ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. [PERSON] et al. [24] set the fingerprinting dataset as several images. They used a CNN to extract reliable features from the images and then built internal representations between images and the locations of reference points based on the PyTorch computational framework. Some researchers also used ANNs [25] and multilayer perceptron networks [26] for Wi-Fi-based positioning inside buildings. [PERSON] et al. [27] implemented an RNN for indoor positioning. However, their proposed system does not use Wi-Fi signals; it uses the magnetometer only. The studies examined so far attempted to solve the problems with traditional approaches, such as by reducing the execution time that can occur from large data sizes, removing manual parameter tuning, and reducing positioning inaccuracies resulting from signal fluctuations when using a neural network.