Application of Kalman Filter to the uncertainty of Model Resistance Data obtained from experiment

Standard deviation is the correct way to characterise the spread of the data and as
\nthe uncertainty associated with measurement the value of the standard deviation
\nmay be refined. The aim is to quantify the level of uncertainty in the resistance data
\nof a model tanker obtained from towing tank tests. Kalman Filter (KF) was used to
\ncorrect the standard deviation of the data, which is composed of the state-space
\nmodel and least-squares method. Results of the simulations showed that KF could
\ndecrease the standard deviation of the resistance for a range of speeds (1,029-1.543
\nm/s). The standard deviation of filtered data is much smaller (1.3%-4.2%) than that
\nof unfiltered data (14.7%-28.4%). The proposed filter method can therefore reduce
\nthe uncertainty of the model experiment