Design and verification of the ship attitudes measuring and monitoring and analysis system

Abstract

It is of significant importance to monitor the attitude of ship during navigation and operations. The three uppermost parameters of the attitude monitoring are roll, pitch and heave. For measuring the above parameters accurately, a sensor is designed in this paper. First, the attitude algorithm of this sensor is learned. A modified Rodrigues parametrisation-based method is developed in solving the attitude algorithm and extended Kalman filter algorithm is used to achieve the optimal value. The sensor calibration model was studied too. Second, in order to observe and measure the results more conveniently and intuitively, a user-friendly monitoring system is developed. Due to the development of the corresponding analysis system, the measured results can be analysed for a better understanding of the characteristics of the ship. Specially, the ship command parameters are as the input of the sensor and the corresponding closed-loop measure, and control system is designed to solve the problem that the current study cannot eliminate the effects of the attitude measurement inaccuracy of the sensor very well owing to the ship's acceleration. The control strategy is verified by the sea trials because the attitude measurement error caused by the ship's acceleration is significantly reduced. The accuracy of the designed attitude sensor is also verified. More importantly, the application prospects of the designed are also illustrated in this paper.

Keywords:

• ship attitude sensor
• ship attitude measuring
• ship attitude monitoring
• ship attitude analysis

Acknowledgements

The authors are grateful for the technical support from the Sentera Technology Corporation.

Additional information

Funding

The authors are grateful for the financial support from the China National Major Projects of Science and Technology [grant number 2011ZX05027-002]; the National Basic Research Program of China [grant number 2011CB013702]; the National Natural Science Foundation of China [grant number 51349002] and the National Natural Science Foundation of China [grant number 11302264].