WeChat Mini Program
Old Version Features

Uncertainty Analysis of Initial Orbit Determination in the Minimum Radar Admissible Region.

Yasheng Zhang, Jinyan Xue, Xuefeng Tao, Shuailong Zhao

Scientific reports(2025)

Space Engineering University

Cited 0|Views0
Abstract
The poor accuracy of the single solution of the initial orbit determination (IOD) affects the precise orbit determination, target matching and arc-segment correlation. This paper proposes a method for analyzing the uncertainty of the IOD results based on the Minimum Radar Admissible Region (MRAR), which describes the IOD results in the form of probability in the region bounded by the minimum admissible region. The MRAR can contain the real orbit state with a probability greater than 99.7%; the weight coefficients of each sampling point in the MRAR are calculated by using the Bayesian formula, and the probability density function of the IOD is obtained by using the weighted kernel density estimation method. The simulation experiment proves that the proposed method can well describe the possibility of each point in the MRAR to be the real orbit state, and can provide a better initial value for the subsequent space mission analysis.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • 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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined