Highly accurate estimation of a ship's position (1st report)

-case to use only GPS-

Abstract

In this paper, a new technique for an estimation of a ship's position is introduced. In the past, an estimation method of a ship's position by using Kalman Filter (KF) has been already proposed. However, when an observed position signal with GPS has a large noise component, the accuracy of the estimated position by using KF is wrong because the probability density function of the observation noise does not satisfy the assumption of Gaussian sequence. In order to solve this problem, we introduce sequential data assimilation by using Monte Carlo Filter (MCF). Moreover, in this study, we assume Cauchy distribution as the probability density function of the observation noise. The verification of the proposed procedure is carried out by numerical experiments and onboard experiments. From the comparison of the proposed procedure and the procedure by using KF, it is confirmed that the proposed procedure can remove the influence of the large noise component.