Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment

• Main Author: Gunwoo Lee
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Applied Sciences
• Subjects: indoor localization; recurrent neural network; hybrid positioning
• Online Access: https://www.mdpi.com/2076-3417/10/14/4721

Accidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is becoming common on land, to enhance maritime safety. Specifically, a recurrent neural network (RNN)-based hybrid localization system (RHLS) that provides accurate and efficient user-tracking results is proposed. RHLS performs hybrid positioning by receiving wireless signals, such as Wi-Fi and Bluetooth, as well as inertial measurement unit data from smartphones. It utilizes the RNN to solve the problem of tracking accuracy reduction that may occur when using data collected from various sensors at various times. The results of experiments conducted in an offshore environment confirm that RHLS provides accurate and efficient tracking results. The scalability of RHLS provides managers with more intuitive monitoring of assets and crews, and, by providing information such as the location of safety equipment to the crew, it promotes welfare and safety.