Prediction Algorithm for Satellite Instantaneous Attitude and Image Pixel Offset Based on Synchronous Clocks

Abstract

To ensure a high signal-to-noise ratio and high image volume, a geostationary orbiting ocean remote-sensing system needs to maintain high platform stability over a long integration time because it is affected by satellite attitude changes. When the observation target is the ocean, it is difficult to extract image features because of the lack of characteristic objects in the target area. In this paper, we attempt to avoid using image data for satellite attitude and image pixel offset estimation. We obtain the satellite attitude by using equipment such as gyroscopes and performing time registration between the satellite attitude and the image data to achieve pixel offset matching between images. According to the law of satellite attitude change, we designed a Kalman-like filter fitting (KLFF) algorithm based on the satellite attitude change model and the Nelder–Mead search principle. The discrete attitude data were time-marked by a synchronization system, and high-precision estimation of the satellite attitude was achieved after fitting with the KLFF algorithm. When the measurement accuracy of the equipment was 1.0 × 10−3°, the average prediction error of the algorithm was 1.09 × 10−3°, 21.58% better than the traditional interpolation prediction result of 1.39 × 10−3°. The peak value of the fitting angle error reached 2.5 × 10−3°. Compared with the interpolation prediction result of 6.2 × 10−3°, the estimated stability of the satellite attitude improved by about 59.68%. After using the linear interpolation method to compensate for the estimated pixel offset, its discrete range was 0.697 pixels. Compared with the 1.476 pixels of the interpolation algorithm, it was 52.8% lower, which improved the noise immunity of the algorithm. Finally, a KLFF algorithm was designed based on the satellite attitude change model by using external measurement data and the synchronous clock as a benchmark. The instantaneous attitude of the satellite was accurately estimated in real time, and the offset matching between the images was realized, which lays the foundation for in-orbit satellite data processing.