Sea state estimation from inertial platform data for real-time ocean wave prediction
| dc.contributor.advisor | Boje, Edward | |
| dc.contributor.advisor | Verrinder, Robyn A | |
| dc.contributor.author | Gwatiringa, Tinashe G | |
| dc.date.accessioned | 2019-02-11T13:55:29Z | |
| dc.date.available | 2019-02-11T13:55:29Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-02-11T08:16:28Z | |
| dc.description.abstract | Ocean observation is vital in understanding how the oceans contribute toward climate change and other effects. This is one of many undertakings requiring a persistent presence in the oceans. These maritime activities are mainly carried out on large research vessels chartered for weeks at a time, which can be extremely costly. In addition, the data obtained when using these vessels are only short snapshots of the continual processes that occur. Recently, there has been a drive toward using Unmanned Surface Vehicles (USVs) and Unmanned Underwater Vehicles (UUVs), which can be deployed at a fraction of the cost, and provide greatly improved spatio-temporal data. The wave glider (WG) is one such autonomous marine robot used for persistent ocean research and other maritime activities, and forms the focus of this study. The WG is a low power USV/UUV hybrid that harnesses wave energy for propulsion, and has a small solar- and battery-powered thruster, and a rudder for steering. Due to effects of waves, currents, and other disturbances, the platform tends to veer off its desired path. Additionally, local sea state information is not taken into consideration while manoeuvring, hence energy extraction from ocean waves is not optimal. More sophisticated navigation algorithms operating on a per-wave strategy may improve accuracy along a specified path and maximise the energy uptake from the waves. To realise these improvements requires prediction of local wave behaviour. If one can predict what the wave field will be a short time in the future, then possible control action can be taken to efficiently navigate in the environment. Inertial measurements and wave modelling have been used to improve localisation of the WG platform directly, and predict the platform’s velocity. However there is limited work in the context of WG navigation. Hence the problem this dissertation aims to solve is the estimation and subsequent prediction of local wave behaviour. This work proposes a novel approach to estimate the sea state and hence predict short-term, local wave behaviour from inertial measurements on a slow-moving marine platform such as the WG. A Kalman filtering strategy consisting of a phase-locked loop and filter based sea state estimator is used to generate local height and angle of arrival estimates. This method offers an improvement over existing Fast Fourier Transform methods as it does not require long time series data to produce results, and enables the prediction of wave behaviour a short time into the future. The ideas are tested in simulation by generating wind waves using ocean wave models such as the Pierson Moskowitz model, and dynamic a dynamic model of the WG platform. In addition, a small scale lab experiment is carried out to verify the performance of the sea-state estimator developed. Preliminary results obtained indicate that relative wave height can be estimated on-board a marine platform, using only inertial sensors. | |
| dc.identifier.apacitation | Gwatiringa, T. G. (2018). <i>Sea state estimation from inertial platform data for real-time ocean wave prediction</i>. (). University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/29496 | en_ZA |
| dc.identifier.chicagocitation | Gwatiringa, Tinashe G. <i>"Sea state estimation from inertial platform data for real-time ocean wave prediction."</i> ., University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018. http://hdl.handle.net/11427/29496 | en_ZA |
| dc.identifier.citation | Gwatiringa, T. 2018. Sea state estimation from inertial platform data for real-time ocean wave prediction. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Gwatiringa, Tinashe G AB - Ocean observation is vital in understanding how the oceans contribute toward climate change and other effects. This is one of many undertakings requiring a persistent presence in the oceans. These maritime activities are mainly carried out on large research vessels chartered for weeks at a time, which can be extremely costly. In addition, the data obtained when using these vessels are only short snapshots of the continual processes that occur. Recently, there has been a drive toward using Unmanned Surface Vehicles (USVs) and Unmanned Underwater Vehicles (UUVs), which can be deployed at a fraction of the cost, and provide greatly improved spatio-temporal data. The wave glider (WG) is one such autonomous marine robot used for persistent ocean research and other maritime activities, and forms the focus of this study. The WG is a low power USV/UUV hybrid that harnesses wave energy for propulsion, and has a small solar- and battery-powered thruster, and a rudder for steering. Due to effects of waves, currents, and other disturbances, the platform tends to veer off its desired path. Additionally, local sea state information is not taken into consideration while manoeuvring, hence energy extraction from ocean waves is not optimal. More sophisticated navigation algorithms operating on a per-wave strategy may improve accuracy along a specified path and maximise the energy uptake from the waves. To realise these improvements requires prediction of local wave behaviour. If one can predict what the wave field will be a short time in the future, then possible control action can be taken to efficiently navigate in the environment. Inertial measurements and wave modelling have been used to improve localisation of the WG platform directly, and predict the platform’s velocity. However there is limited work in the context of WG navigation. Hence the problem this dissertation aims to solve is the estimation and subsequent prediction of local wave behaviour. This work proposes a novel approach to estimate the sea state and hence predict short-term, local wave behaviour from inertial measurements on a slow-moving marine platform such as the WG. A Kalman filtering strategy consisting of a phase-locked loop and filter based sea state estimator is used to generate local height and angle of arrival estimates. This method offers an improvement over existing Fast Fourier Transform methods as it does not require long time series data to produce results, and enables the prediction of wave behaviour a short time into the future. The ideas are tested in simulation by generating wind waves using ocean wave models such as the Pierson Moskowitz model, and dynamic a dynamic model of the WG platform. In addition, a small scale lab experiment is carried out to verify the performance of the sea-state estimator developed. Preliminary results obtained indicate that relative wave height can be estimated on-board a marine platform, using only inertial sensors. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Sea state estimation from inertial platform data for real-time ocean wave prediction TI - Sea state estimation from inertial platform data for real-time ocean wave prediction UR - http://hdl.handle.net/11427/29496 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/29496 | |
| dc.identifier.vancouvercitation | Gwatiringa TG. Sea state estimation from inertial platform data for real-time ocean wave prediction. []. University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/29496 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Electrical Engineering | |
| dc.title | Sea state estimation from inertial platform data for real-time ocean wave prediction | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |