Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism
This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing a...
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Format: | Article |
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MDPI AG
2022-08-01
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Series: | Journal of Marine Science and Engineering |
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Online Access: | https://www.mdpi.com/2077-1312/10/9/1210 |
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author | Chunyu Song Xianku Zhang Guoqing Zhang |
author_facet | Chunyu Song Xianku Zhang Guoqing Zhang |
author_sort | Chunyu Song |
collection | DOAJ |
description | This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models. |
first_indexed | 2024-03-09T23:32:46Z |
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id | doaj.art-0f0e81e923e647bab22a2c9467218eb4 |
institution | Directory Open Access Journal |
issn | 2077-1312 |
language | English |
last_indexed | 2024-03-09T23:32:46Z |
publishDate | 2022-08-01 |
publisher | MDPI AG |
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series | Journal of Marine Science and Engineering |
spelling | doaj.art-0f0e81e923e647bab22a2c9467218eb42023-11-23T17:06:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-08-01109121010.3390/jmse10091210Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting MechanismChunyu Song0Xianku Zhang1Guoqing Zhang2Navigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaThis paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models.https://www.mdpi.com/2077-1312/10/9/1210ship maneuveringnonlinear innovationreal-time predictionExtended Kalman Filterforgetting factorfull-scale test |
spellingShingle | Chunyu Song Xianku Zhang Guoqing Zhang Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism Journal of Marine Science and Engineering ship maneuvering nonlinear innovation real-time prediction Extended Kalman Filter forgetting factor full-scale test |
title | Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism |
title_full | Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism |
title_fullStr | Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism |
title_full_unstemmed | Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism |
title_short | Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism |
title_sort | nonlinear innovation based maneuverability prediction for marine vehicles using an improved forgetting mechanism |
topic | ship maneuvering nonlinear innovation real-time prediction Extended Kalman Filter forgetting factor full-scale test |
url | https://www.mdpi.com/2077-1312/10/9/1210 |
work_keys_str_mv | AT chunyusong nonlinearinnovationbasedmaneuverabilitypredictionformarinevehiclesusinganimprovedforgettingmechanism AT xiankuzhang nonlinearinnovationbasedmaneuverabilitypredictionformarinevehiclesusinganimprovedforgettingmechanism AT guoqingzhang nonlinearinnovationbasedmaneuverabilitypredictionformarinevehiclesusinganimprovedforgettingmechanism |