A new integrated Gaussian-Markov process model for precision shipboard transfer alignment

In shipboard transfer alignment (TA), Kalman filter used to estimate the misalignment angle requires accurate ship dynamic flexure model. Traditionally, the ship dynamic flexure is modelled as a second-order Gaussian-Markov process according to experience. However, this model has not been validated in real applications, when the current model is differ with this used in Kalman filter design will result in a large measurement error. To solve this problem, an integrated Gaussian-Markov process model is proposed in this contribution which is based on the hydroelastic analysis and statistic from previous measured data. Specifically, theoretical analysis shows ship dynamic flexure is the response of elastic ship hull to sea wave loads, while the sea wave spectrum is of double-peaked in frequency filed for the swell and wind sea waves occurring simultaneously, and therefore, the dynamic flexure is also with double-peaked spectrum distribution. Furthermore, the frequency analysis based on our previous measured ship dynamic flexure data also demonstrates the power spectrum density (PSD) of actual dynamic flexure angle is of double-peaked distribution, which can be modelled more accurately by combined using two independent second-order Gaussian-Markov process models. Experimental results show that Kalman filter utilizing the proposed integrated Gaussian-Markov process model provides more accurate measurement of the misalignment angle compared with using traditional second-order Gaussian-Markov process model in shipboard TA.