Real-Time Heave Motion Estimation using Adaptive Filtering Techniques

Richter, Markus; Schneider, Klaus; Walser, Dominik; Sawodny, Oliver

doi:10.3182/20140824-6-za-1003.00111

Cited by 22 publications

(5 citation statements)

References 11 publications

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“…One example is Godhavn (1998), where an IMU was utilized together with a bandpass filter motivated by that the average heave position of a marine surface craft is zero. The technique of Godhavn (1998) was modified by Richter et al (2014) to compensate for amplitude and phase errors by using additional adaptive filters. Another heave estimation strategy, based on the assumption that the heave motion could be described by a sum of cosines, while applying an accelerometer, was presented in Küchler et al (2011) and also applied in Auestad et al (2013).…”

Section: Introductionmentioning

confidence: 99%

A virtual vertical reference concept for aided inertial navigation at the sea surface

Bryne

Rogne

Fossen

et al. 2018

Control Engineering Practice

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When trying to capture the heaving motion of a marine vessel in waves, conventional position references such as global navigation satellite systems fall short in many cases. Because of design and satellite geometry, the vertical position measurement is typically inferior in both accuracy and precision compared to its horizontal counterparts. For aiding an inertial navigation system (INS), using conventional vertical position references may therefore be suboptimal. This article presents an alternative algorithm utilizing a virtual vertical reference (VVR) concept, based on the mean sea level, to aid the INS. The novelty of the algorithm arises where the VVR concept is augmented with an error model, used to improve the heave estimation performance. The motion of the vessel is estimated using an INS, employing a feedback-interconnected observer framework, based on nonlinear theory. The INS, using low-cost micro-electro-mechanical-system-based inertial measurement units, provides position, velocity and attitude in a deadreckoning fashion, and is aided by a horizontal position reference, the VVR, and a compass to prevent the estimates from drifting. The estimation performance obtained with the observer structure is evaluated using Monte Carlo simulations and compared to preceding results. The algorithm is also validated experimentally and compared to an industry standard vertical reference unit using data collected on board an offshore vessel.

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Section: Introductionmentioning

confidence: 99%

A virtual vertical reference concept for aided inertial navigation at the sea surface

Bryne

Rogne

Fossen

et al. 2018

Control Engineering Practice

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“…Examples of heave estimation based on MEMS inertial sensors, using both linear and nonlinear methods, are presented in [22], [23], [27]- [29]. [27] and [29] are based on linear bandpass filtering, while [22], [23] is applying NLOs to estimate heave. In [24], a more sophisticated method evolving from [22], [23] is used, estimating wave motion parameters and employing these in the estimator.…”

Section: B Nonlinear Estimationmentioning

confidence: 99%

On the Usage of Low-Cost MEMS Sensors, Strapdown Inertial Navigation, and Nonlinear Estimation Techniques in Dynamic Positioning

Rogne

Bryne

Fossen

et al. 2021

IEEE J. Oceanic Eng.

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In this article we suggest that a strapdown inertial navigation system based on MEMS inertial sensors is a useful addition to a vessel with dynamic positioning. We conduct full-scale experiments with MEMS inertial sensors on board a Dynamically Positioned (DP) offshore vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.

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“…Severe lifting movement could seriously affect normal offshore operations, such as cargo replenishment between ships, engineering ships' deep-sea operations, and take-off and landing of ship-borne helicopters [3]. When cranes on engineering ships carry out lifting operations on the transmission system of natural gas and oil pipelines located on the seabed, the ship movement caused by waves seriously affects the regular operation of the lifting system and even leads to accidents [4]. Therefore, improving the prediction performance of ship rocking posture can ensure the safety of the ship's work in strong winds and huge waves and effectively improve the use efficiency of naval shipborne weapons and equipment.…”

Section: Introductionmentioning

confidence: 99%

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

et al. 2022

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In recent years, with the development of modern technology and industrialization, the shipbuilding industry has become an essential field in a country. Offshore operations, such as helicopter takeoff and landing, and ship-to-ship cargo replenishment, need an accurate prediction of ship motion attitude, not only to improve efficiency but also to protect crew members' lives. However, using the single model has limited influence on prediction. Therefore, this paper proposed a Cross-Parallel optimization algorithm framework, which made full use of the development ability of the Harris Hawk optimization algorithm (HHO) and the searching ability of the sine-cosine algorithm (SCA). In this paper, we use Rosenbrock and Rastrigin functions for testing. The results show that the cross-parallel optimization algorithm has faster search speed and stronger optimization capability. The optimization algorithm is used to optimize the hyperparameters of long short-term memory (LSTM) neural networks. It improves the network's training ability to predict the ship's attitude. The proposed method in this paper is compared with 9 models such as BP, LSTM, GRU, BILSTM and TCN. It is tested with three degrees of freedom under different sea states. MAE and RMSE are used as the evaluation indexes of this paper. In the six sets of experimental results, compared with the other nine models, the method in this paper can reduce the MAE by up to 9.2%, 7.2%, 4.7%, 5.4%, 6.4%, and 6.8%, respectively. When using RMSE as the evaluation index, the decrease was at most 10.1%, 8.7%, 5.6%, 7.1%, 7.5%, and 8.2%. The experimental results show that the method in this paper has the highest prediction accuracy.

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Real-Time Heave Motion Estimation using Adaptive Filtering Techniques

Cited by 22 publications

References 11 publications

A virtual vertical reference concept for aided inertial navigation at the sea surface

A virtual vertical reference concept for aided inertial navigation at the sea surface

On the Usage of Low-Cost MEMS Sensors, Strapdown Inertial Navigation, and Nonlinear Estimation Techniques in Dynamic Positioning

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

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