The data acquired by the satellite positioning receiver in the trajectory correction projectile must be filtered in real-time to predict the point. The calculation of traditional filtering method is time-consuming, and is difficult to meet the requirements of real-time filtering. A kind of extended Kalman filtering algorithm based on polynomial fitting was proposed. The data of projectile flight in the time interval was replaced by the fitting interpolation data. In this way the filter frequency could be reduced. Simulation results show that the computation time of the proposed method can be reduced by 7/8 compared to traditional extended Kalman filtering without reducing the filtering precision, and the real-time performance is improved. This method can provide important reference for the research of key technology of trajectory correction projectile. At the same time, the method can be applied to other filtering algorithms, and has a strong portability.

Concerning the poor performance in anti-noise and the limitations of the fixed partition of the phase-grouping method, an adaptive phase partitioning method was proposed. Firstly, this paper used the adaptive smoothing and Wallis filtering for image pre-processing and adopted the Canny method for edge detection; secondly, selected the optimal partitioning of the image based on the adaptive phase partitioning method to create the line supporting area; finally, fitted and merged the lines by the least squares method. The experimental results show that the proposed method can effectively solve the line breakage and mistaken extraction caused by the noise and fixed partition, and this method extracts line completely and precisely.