基于VLBI的高精度定位

GUO Li,ZHANG Yu,LI Jinling,WANG Guangli,ZHENG Weimin

cnki（2021）

中国科学院上海天文台

Cited 2|Views1

Abstract

基于甚长基线干涉测量技术（Very Long Base Interferometer,VLBI）的时延、时延率以及USB（United S-band）/UXB（United X-band）的测距特点,采用联合统计定位及月面高程约束策略,实现了高精度的探测器的实时单点定位和准实时联合统计定位,且实时单点定位不受力学约束能够快速准确地给出三维位置信息,最终实现在轨控弧段和探测器被月球捕获阶段的轨道根数的实时监测。在"嫦娥3号"月面着陆器定位中,获得月面位置好于100 m的外符合精度。在特殊的轨道阶段,采用△DOR差分技术,VLBI探测器近月捕获制动及"嫦娥4号"中继星变轨进入地月L2平动点的Halo轨道等特殊阶段,实时快速地给出6个瞬时轨道根数,为工程提供重要参考。

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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