The existing similarity-based moving target trajectory prediction algorithms are generally classified according to the spatial-temporal characteristics of the data， and the characteristics of the algorithms themselves cannot be reflected. Therefore， a classification method based on algorithm characteristics was proposed. The calculation of the distances between two points is required for the trajectory similarity algorithms to carry out the subsequent calculations， however， the commonly used Euclidean Distance （ED） is only applicable to the problem of moving targets in a small region. A method of similarity calculation using geodetic distance instead of ED was proposed for the trajectory prediction of sea targets moving in a large region. Firstly， the trajectory data were preprocessed and segmented. Then， the discrete Fréchet Distance （FD） was adopted as similarity measure. Finally， synthetic and real data were used to test. Experimental results indicate that when sea targets move in a large region， the ED-based algorithm may gain incorrect prediction results， while the geodetic distance-based algorithm can output correct trajectory prediction.
