A method of real-time data reduction of asteroid optical observation image based on GPU

Ying WU; Liang GE; Xiao-meng LU; Jian-feng TIAN; Hui-juan WANG; Xiao-jun JIANG

doi:10.37188/OPE.20202811.2527

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A method of real-time data reduction of asteroid optical observation image based on GPU

Monitoring and Early Warning of NEOs | 更新时间：2020-12-09

- A method of real-time data reduction of asteroid optical observation image based on GPU
- Optics and Precision Engineering Vol. 28, Issue 11, Pages: 2527-2534(2020)
- 作者机构：
  
  1.中国科学院光学天文重点实验室(国家天文台), 北京 100101
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20202811.2527
  CLC： V528
- Received：20 April 2020，
  
  Revised：25 May 2020，
  
  Accepted：25 May 2020，
  
  Published：25 November 2020
- 稿件说明：
移动端阅览
Ying WU, Liang GE, Xiao-meng LU, et al. A method of real-time data reduction of asteroid optical observation image based on GPU[J]. Optics and precision engineering, 2020, 28(11): 2527-2534.
DOI：

Ying WU, Liang GE, Xiao-meng LU, et al. A method of real-time data reduction of asteroid optical observation image based on GPU[J]. Optics and precision engineering, 2020, 28(11): 2527-2534. DOI： 10.37188/OPE.20202811.2527.

摘要

小行星地基光学观测是小行星搜寻和性质研究的重要手段。近年来，小行星搜寻项目朝着大口径大视场的方向发展，探测能力提升的同时数据量也大大增长，为了提高小行星光学观测图像的处理速率，本文提出基于GPU的小行星光学观测图像实时处理方法。对Source Extractor算法进行研究，在满足提取精度的前提下，实现Source Extractor算法的简化和基于GPU的并行化；同时，采用Match匹配算法进行天文定位，并对Match匹配算法进行优化，提高算法适用性和准确率。实验结果表明：利用NVIDA GeForce GTX 2080Ti显卡搭建的实验平台实现简化和并行化后的算法，相比于CPU下的串行算法，提速比可达17（S/N阈值设置为3），且随着高性能显卡的进一步发展，提速比还有提升空间，此方法还适用于其他光学巡天观测图像的处理。

Abstract

Ground-based optical observation is an important means of asteroid detection and property research. In recent years

asteroid survey projects have developed toward larger apertures and larger fields of view. While the detection capability has increased

the amount of data has also increased greatly. To improve the speed of asteroid data reduction

a graphics-processing-unit-based real-time processing method for asteroid optical observation images was proposed. Based on a previous target extraction algorithm

we realized the parallelization of target extraction

researched the Source Extractor algorithm

simplified and parallelized the Source Extractor code

and improved the Match algorithm to improve both applicability and accuracy. The experimental results show that the simplified

parallelized algorithm implemented in an experimental platform built based on the NVIDA GeForce GTX 2080Ti graphics card offers an increase in speed by a factor of up to 17 (with the signal-to-noise threshold set to 3) compared with that of a serial algorithm running on only a central processing unit. With the further development of high-performance graphics cards

there is room for improvement in speedup ratio

and this method is also suitable for the processing of other optical survey observations.

关键词

Keywords

references

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