Abstract
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.
Highlights
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
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
The tracking accuracy achieved by the RNN-based hybrid localization system (RHLS) was compared with that of a model built in a supervised manner using the ground truth location labels
Summary
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. There is a limitation in that positioning accuracy is difficult to improve beyond a certain value These difficulties were solved using various sensors installed on smartphones. If the learning data are densely collected, it is possible to improve the accuracy by analyzing the signal distribution in advance at a specific location, but there may be cases in which the IMU sensor data cannot be used, because the amount of computation increases rapidly during positioning. It is difficult to improve accuracy if the different data collection cycles for each sensor are not properly synchronized To solve this problem, this paper proposes a method incorporating a localization engine that uses an artificial neural network (ANN), which creates a learning model when learning data are given, before classifying the new data.
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