A Method for Continuous Observation of Sea Surface Moving Targets Based on Multi Point Trajectory Prediction

The detection and tracking of sea surface sensitive targets are challenging due to their high manoeuvrability and the limited field of view of low-orbit remote sensing satellites. This paper presents a prediction model for the trajectory of moving targets on the sea surface, which utilizes a dynamic and variable potential target area and transition probability density function by using prior information, such as the position, speed, and course of a moving target when observed. When a moving target is observed multiple times, its velocity is estimated using the least squares method and the Kalman filtering algorithm, while its course is predicted using Hermite interpolation polynomials. Simulation examples are used to validate the feasibility and effectiveness of the proposed approach.