Measurement And Prediction Of Ship Motion Attitude

There are six degrees of freedom irregular motions of ship under the disturbance of sea wind and waves: roll,pitch,yaw,sway,surge and heave.The motion conditions are complex and changeable,among which roll,pitch and heave(heave)have the most critical influence on the safety and efficient navigation of the ship.Design and implement the ship motion measurement system of roll,pitch and heave,which can measure the ship’s motion state on the water surface in real time,realize the on-line monitoring of the ship’s motion,and provide guarantee for the ship’s motion attitude control..Ship motion attitude prediction is an important branch of ship research.If the rolling angle of ship motion can be predicted a few seconds in advance,it will be of great significance to design the anti rolling device and improve the anti rolling effect.It only needs to use the historical data of the ship’s motion attitude to find the rules for modeling and prediction.In this paper,a motion measurement system based on MEMS(micro electro mechanical system)is designed,and the data collected from this system is applied to the recursive estimation of AR model parameters,the ship motion prediction model is established,and its simulation analysis is carried out.Firstly,the concept and development trend of MEMS are introduced,the measurement principle and application scope of several commonly used motion measurement methods are analyzed,and the strapdown inertial motion measurement scheme is selected by comparison.Aiming at the selected measurement scheme,three motion attitude updating algorithms are studied,including Euler angle method,direction cosine method and quaternion method,and the three updating algorithms are compared,Then the error between gyroscope and accelerometer and the compensation method are analyzed.Secondly,the MEMS product of French SBG company,ellise2-a-g4a3-b1,is selected,its parameter characteristics are introduced,the upper computer suitable for ship motion measurement system is compiled,the software composition and operation process are introduced,and data collection is carried out.The collected data are used in static and dynamic experiments respectively,which verifies that the measurement accuracy of the system is good.Finally,for the linear discrete multisensor system with the same state dependent multiplicative noise and uncertain noise variance and linear correlation of white noise,the virtual noise method is used to transform the model.Then,based on the minimax robust estimation principle,the robust Kalman filter of robust state fusion is derived according to the matrix weighting rule,and it is proved that the actual Kalman filter of state fusion is robust.It is applied to the recursive estimation of AR model parameters,and the ship motion prediction model is established,and the simulation analysis is carried out.The prediction results of AR prediction model based on robust state fusion Kalman filter for linear discrete multisensor system with uncertain noise variance and linear correlation of white noise are compared.