A Cluster-based Parallel Doppler Search Algorithm for Deep Space Exploration Applications

Abstract

The orbit predictions are subject to considerable errors due to the orbital maneuvers and systematic errors of the deep space probes. The detector signals are Doppler-shifted, so the correlated data cannot be integrated for a long time to achieve a higher signal-to-noise ratio. In addition, the computation of correlation increases with the increase of ground tracking stations, space detectors, and correlation channels. In this paper, we propose a cluster based Doppler search technique. First, a priory model is used to run fringe rotation and phase compensation relative to the center of the Earth. Second, a phase structure approach is applied to solve the Doppler frequency problem with high accuracy. Finally, a least square method is used to generate a polynomial model as the input to the correlation model. Due to the multi-channel signal and many grounds receiving stations, the data calculation can be enriched with Doppler search methods at the theoretical level and specific parallel algorithm design solutions at the application level. Through the analysis of the algorithms; the search speed can be improved by using cluster-distributed computing. The results of this study can help to improve the determination of deep space probe orbit capability by differential VLBI technology for application in deep space exploration in China.