Statistical learning modeling method for space debris photometric measurement

Wenjing Sun, Jinqiu Sun, Yanning Zhang, Haisen Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

Original languageEnglish
Title of host publicationSelected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
EditorsHesheng Chen, Jianyu Wang, Jialing Le, Jianda Shao, Yueguang Lv
PublisherSPIE
ISBN (Electronic)9781510610118
DOIs
StatePublished - 2017
EventChinese Society for Optical Engineering Conferences, CSOE 2016 - Jinhua, Suzhou, Chengdu, Xi'an, and Wuxi, China
Duration: 1 Nov 2016 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10255
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceChinese Society for Optical Engineering Conferences, CSOE 2016
Country/TerritoryChina
CityJinhua, Suzhou, Chengdu, Xi'an, and Wuxi
Period1/11/16 → …

Keywords

  • Least squares
  • Measurement accuracy
  • Photometric measurement
  • Statistical learning

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