An approach to improve the offshore platform coordinates accuracy by using multichannel Kalman filtering

Area of Science:

• Ocean Engineering
• Control Systems
• Signal Processing

Background:

• Offshore platforms (OPs) experience complex motions from environmental factors like wind, undercurrent, and sea waves.
• Accurate estimation of OP motion is crucial for operational stability and safety.
• Existing methods may not fully capture the dynamics of combined low-frequency and high-frequency motions.

Purpose of the Study:

• To design and evaluate multichannel Kalman filters for precise offshore platform coordinate estimation.
• To model and account for both low-frequency (wind, undercurrent) and high-frequency (sea) motions.
• To develop adaptive filtering techniques for real-time parameter estimation and control.

Main Methods:

• Development of a mathematical model for low-frequency OP motion using differential equations.
• Representation of high-frequency OP motion using a moving-average multivariable autoregression model.
• Implementation of two jointly operating Kalman filters for parameter estimation of both motion components, with adaptive parameter adjustment.

Main Results:

• Successful design and simulation of two algorithms (parallel and data compression) for multichannel OP motion parameter estimation.
• Demonstration of adaptive filter parameter adjustment to environmental disturbances.
• Validation of the proposed Kalman filter approach for offshore platform coordinate estimation.

Conclusions:

• Multichannel Kalman filters provide an effective method for estimating offshore platform coordinates under combined motion dynamics.
• The adaptive nature of the filters enhances robustness against sea-induced disturbances.
• The developed algorithms offer practical solutions for real-time monitoring and control of offshore platforms.