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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Main Authors: Chunyu Song, Xianku Zhang, Guoqing Zhang
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Journal of Marine Science and Engineering
Subjects:
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.
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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