Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter
Ship operators typically depend on the visual method for draft reading, which may lead to errors or approximations, and it further introduces draft survey calculation errors and can not provide continuous updates. Ensuring accurate real-time ship draft measurement becomes crucial for enhancing navig...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MULTIPHYSICS
2023-12-01
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| Series: | International Journal of Multiphysics |
| Online Access: | https://journal.multiphysics.org/index.php/IJM/article/view/895 |
| _version_ | 1827611196182757376 |
|---|---|
| author | S Dhar H Khawaja |
| author_facet | S Dhar H Khawaja |
| author_sort | S Dhar |
| collection | DOAJ |
| description | Ship operators typically depend on the visual method for draft reading, which may lead to errors or approximations, and it further introduces draft survey calculation errors and can not provide continuous updates. Ensuring accurate real-time ship draft measurement becomes crucial for enhancing navigational safety, optimizing vessel performance, and achieving precise cargo and consumable measurements. With the current approach towards automation and remote operation of ships, the need to rely on the accuracy of the measurements provided by the sensors has increased. Although different sensor types are gradually being adopted for draft measurement, they encounter challenges in the demanding marine environment which may result in noisy and inaccurate readings. This paper aims to estimate the true draft of a ship in different conditions from noisy sensor measurements using the Kalman filter algorithm. The purpose of the algorithm is to reduce uncertainty in draft measurement that is generated from inaccuracies in the sensor or from the dynamic marine environment. The paper involves designing the Kalman Filter algorithm for draft measurement to work within the different conditions the ship may experience. Simulating different situations and analyzing the result, the application of the filter shows the advantage in real-time draft measurement in both static and dynamic conditions. |
| first_indexed | 2024-03-09T08:03:35Z |
| format | Article |
| id | doaj.art-6ee24dd0794042769ea6911d52135d10 |
| institution | Directory Open Access Journal |
| issn | 1750-9548 2048-3961 |
| language | English |
| last_indexed | 2024-03-09T08:03:35Z |
| publishDate | 2023-12-01 |
| publisher | MULTIPHYSICS |
| record_format | Article |
| series | International Journal of Multiphysics |
| spelling | doaj.art-6ee24dd0794042769ea6911d52135d102023-12-03T00:35:42ZengMULTIPHYSICSInternational Journal of Multiphysics1750-95482048-39612023-12-0117440742510.21152/1750-9548.17.4.407894Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman FilterS DharH KhawajaShip operators typically depend on the visual method for draft reading, which may lead to errors or approximations, and it further introduces draft survey calculation errors and can not provide continuous updates. Ensuring accurate real-time ship draft measurement becomes crucial for enhancing navigational safety, optimizing vessel performance, and achieving precise cargo and consumable measurements. With the current approach towards automation and remote operation of ships, the need to rely on the accuracy of the measurements provided by the sensors has increased. Although different sensor types are gradually being adopted for draft measurement, they encounter challenges in the demanding marine environment which may result in noisy and inaccurate readings. This paper aims to estimate the true draft of a ship in different conditions from noisy sensor measurements using the Kalman filter algorithm. The purpose of the algorithm is to reduce uncertainty in draft measurement that is generated from inaccuracies in the sensor or from the dynamic marine environment. The paper involves designing the Kalman Filter algorithm for draft measurement to work within the different conditions the ship may experience. Simulating different situations and analyzing the result, the application of the filter shows the advantage in real-time draft measurement in both static and dynamic conditions.https://journal.multiphysics.org/index.php/IJM/article/view/895 |
| spellingShingle | S Dhar H Khawaja Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter International Journal of Multiphysics |
| title | Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter |
| title_full | Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter |
| title_fullStr | Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter |
| title_full_unstemmed | Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter |
| title_short | Real-Time Ship Draft Measurement and Optimal Estimation Using Kalman Filter |
| title_sort | real time ship draft measurement and optimal estimation using kalman filter |
| url | https://journal.multiphysics.org/index.php/IJM/article/view/895 |
| work_keys_str_mv | AT sdhar realtimeshipdraftmeasurementandoptimalestimationusingkalmanfilter AT hkhawaja realtimeshipdraftmeasurementandoptimalestimationusingkalmanfilter |