# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 1 Introduction  
Despite hundreds of ship accidents annually over the past decade [1], a system for disaster response and accident prevention for crew and passengers is lacking. In offshore plant structures, a main ship and a support ship several meters apart are connected by gangways, and there is always the risk of explosion and flame. To cope with this danger via an emergency alarm, a public address/general alarm system and a wireless terminal are used. Nevertheless, if the location of the accident can be identified using a smartphone application, it could be dealt with more efficiently. Workers are always working in areas exposed to danger; thus, if an accident or breakdown is found, workers must be notified promptly. In addition, passengers unfamiliar with the structure of the ship should be provided with evacuation routes and lifeboat location information quickly and accurately. Recently, with the popularization of smartphones, information technology (IT)-based wireless network systems are becoming more common in commercial and passenger ships. Based on this wireless infrastructure, a technology capable of enhancing safety in an emergency using the smartphones of crew members, engineers, and passengers is required. Global navigation satellite system technology can be used outdoors to locate users; however, indoors, this signal can be blocked and difficult to use [2]. To overcome this limitation, many studies for indoor localization were conducted in the past few years. A representative technique for indoor localization is to use a Wi-Fi signal, which uses triangulation between the wireless signals of users and access points (APs), or a signal fingerprinting method, using the strength of wireless signals obtained at specific coordinates [3, 4]. Other methods include beacons using Bluetooth or infrared [5, 6], tag recognition using radio frequency identification (RFID) [7, 8], and quick response (QR) code recognition using a camera [9]. These techniques can derive absolute position coordinates withrelatively high accuracy. However, because the signal scan time of the device is relatively slow or the coordinates can be derived only at the location where the tag is installed, it is difficult to proceed with continuous positioning. In addition, there is a limitation in that positioning accuracy is difficult to improve beyond a certain value. Recently, these difficulties were solved using various sensors installed on smartphones. Specifically, the absolute position coordinates are derived from the above techniques, and the continuous position between these coordinates uses data from inertial measurement unit (IMU) sensors [10; 11]. The simplest approach is to use a loosely coupled technique such as a Kalman filter [12] to weight each sensor's location and average it. However, this method has difficulty in evaluating the weight of each sensor position having different criteria, and there is a difficulty in integrating when a new sensor is additionally used for positioning. On the other hand, probability-based positioning methods using particle filters [13] or hidden Markov models (HMMs) [14] can easily integrate new sensors without considering the weight of each sensor. However, it is necessary to analyze the signal distribution for each space or the probability distribution for sensor accuracy in advance, and this incurs a high cost in terms of the time required to calculate positions. When indoor positioning is performed in a non-line-of-sight area where obstacles exist, the stability of data reception is poor because of the reflection and diffraction of radio waves. The cumulative error for localization increases accordingly. In particular, the internal structure of ships and offshore plants has a narrower structure and more steel than the corridors of ordinary buildings, and electromagnetic interference caused by generators and engine rooms results in serious signal distortions, thereby reducing the accuracy of location recognition.