Online Estimation of Ship Dynamic Flexure Model Parameters for Transfer Alignment

This paper presents an online approach for estimating the dynamic flexure model parameters in shipboard transfer alignment (TA). Traditionally, the application of Kalman filters (KFs) to the TA process is often restricted because of the lack of real-time information on dynamic flexure characteristics, and a KF designed on the basis of inaccurate parameters of the dynamic flexure model will result in a large alignment error. To overcome this difficulty, a parameter estimation algorithm is proposed in this paper, which utilizes the angular increment difference measured by the master inertial navigation system (MINS) and the slave inertial navigation system. Specifically, the Tufts-Kumaresan method is introduced to compute the unknown parameters of the dynamic flexure model from the angular increment correlation function. Our simulation results show that the proposed method can estimate the dynamic flexure parameters with a high degree of accuracy, even in low signal-to-noise ratio conditions. This parameter estimation method does not require a priori knowledge of dynamic flexure characteristics and, therefore, provides the shipboard sensors with an accurate and rapid-response capability for alignment with the MINS.