Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

A hybrid tracking method for maritime obstacles using sensor data

Authors
Kim, Ha-YunRoh, Myung-IlLee, Hye-WonYeo, In-ChangJo, Yeong-MinHa, JisangSon, Nam-Sun
Issue Date
11월-2024
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Maritime obstacle tracking; Extended kalman filter; Learning-based tracking; Ship tracking; Autonomous ship
Citation
OCEAN ENGINEERING, v.312
Journal Title
OCEAN ENGINEERING
Volume
312
URI
https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/10670
DOI
10.1016/j.oceaneng.2024.119242
ISSN
0029-8018
1873-5258
Abstract
Safe navigation of ships depends on the accurate recognition and tracking of nearby maritime obstacles as detected by various sensors. Challenges arise in tracking maritime obstacles from sensor data because of sensor noise and incomplete sensor data. Traditionally, tracking algorithms such as the EKF (Extended Kalman Filter) have been applied to track the state of maritime obstacles, including the position, COG (Course Over Ground), and SOG (Speed Over Ground). This study implemented a combined EKF- and learning-based (hybrid) tracking method. In the EKF-based method, the parameters are related to the uncertainty of the system and the sensor data. These parameters are generally set manually by analyzing the noise of the sensor data and may not be optimal; we optimized the parameters to compensate for this. In the learning-based method, we trained a deep learning model using a DNN (Deep Neural Network) to predict obstacle states from sensor measurement data. We then propose a hybrid tracking method that combines the two tracking methods to compensate for the shortcomings of each method. We verified these three tracking methods using navigation data obtained through field tests. The verification results showed that the learning-based tracking method improved the SOG tracking accuracy by 11.47% compared with the traditional EKF-based tracking method. The tracking accuracy of the hybrid tracking method was reduced by 22.42% for the COG and 42.05% for the SOG. These results indicate that the hybrid tracking method effectively compensates for the limitations of the other methods, resulting in an enhanced tracking performance.
Files in This Item
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Son, Nam Sun photo

Son, Nam Sun
해양공공디지털연구본부
Read more

Altmetrics

Total Views & Downloads

BROWSE