Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration

Stephen Cain

doi:doi:10.11648/j.ajop.20210901.12

| Peer-Reviewed

Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration

Stephen Cain

Published in American Journal of Optics and Photonics (Volume 9, Issue 1)

Received: 10 February 2021 Accepted: 20 February 2021 Published: 3 March 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper introduces a new space object detection algorithm that is designed to process image data taken from astronomical telescopes for the purposes of finding sources of optical radiation in space. Specifically, the algorithm is designed to find unresolvable space objects or objects that possess an angular size that is too small to appear as anything, but a point source as viewed through the telescope conducting the search. The proposed approach involves calibrating the image data into units of photoelectrons and then executing an estimation algorithm to compute the strength of the hypothetical sources in the image. A Likelihood Ratio Test (LRT) is then implemented to make a determination if the hypothetical sources are classified as space objects or not. The proposed algorithm is demonstrated to achieve a higher probability of detecting unresolvable objects than the matched filter, which is still the state-of-the-art approach for finding optical sources in astronomical images. The new approach involves a pre-processing step where the amplitude of the optical source in a given test location is estimated under the hypothesis that at optical source exists at that location. The median filter is used to estimate the background level in the vicinity of the test location. Once these parameters are estimated, A likelihood ratio test is used to determine whether an object is present at the test location. The new algorithm is tested against the matched filter detector using two sets of measured short exposure data of the star Polaris and two stars in its vicinity taken with a small telescope. Receiver Operating Characteristic (ROC) curves are produced for the two detection schemes showing that the new algorithm out-performs the old one with a difference of 10 percent in the probability of detection, which is demonstrated to be statistically significant in these experiments with confidence as high as 90%.

Published in	American Journal of Optics and Photonics (Volume 9, Issue 1)
DOI	10.11648/j.ajop.20210901.12
Page(s)	10-17
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Astronomy, Statistics, Photo-Detector Calibration, Image Processing

References

[1]	Andris D. Jaunzemisa, Dev Minotrab, Marcus J. Holzingerc, Karen M. Feighd, Moses W. Chane, Prakash P. Shenoy, 2017 “Judicial Evidential Reasoning for Decision Support Applied to Orbit Insertion Failure”, 1 st IAA Conference on Space Situational Awareness (ICSSA), Orlando, FL, USA, Nov 2017.
[2]	Joseph Tompkins*, Stephen Cain, and David Becker, 2019, “Near earth space object detection using parallax as multi-hypothesis test criterion," Opt. Express 27, 5403-5419 (2019).
[3]	Pohlig, Stephen C. 1989 "An Algorithm for Detection of Moving Optical Targets." IEEE Transactions on Aerospace and Electronic Systems 25, no. 1 (1989): 56-63.
[4]	J. Chris Zingarelli, Eric Pearce, Richard Lambour, Travis Blake, Curtis J. R. Peterson, and Stephen Cain, 2014 “Improving the Space Surveillance Telescope's Performance Using Multi-Hypothesis Testing”, The Astronomical Journal, Vol. 147, No. 5, pp. 111 (May 2014).
[5]	Viggh, H. E. M., G. H. Stokes, F. C. Shelly, M. S. Blythe, and J. S. Stuart. 1998 "Applying Electro-Optical Space Surveillance Technology to Asteroid Search and Detection: The Linear Program Results." Proceedings of the 1998 Space Control Conference. Lexington, 1998.
[6]	David Becker and Stephen Cain, 2018 "Improved space object detection using short-exposure image data with daylight background," Appl. Opt. 57, 3968-3975 (2018).
[7]	Pearce, E C, F Shelly, and J A Johnson. 2003 "High Precision Real Time Processing for the MOSS and LINEAR Systems." Space Control Conference. Boston, 2003.
[8]	Bertin, E. & Arnouts, S. 1996: SExtractor: Software for source extraction, Astronomy & Astrophysics Supplement 317, 393.
[9]	Tyler Hardy, Travis Blake and, Stephen Cain, 2016 “Unequal a priori probability multiple hypothesis testing in space domain awareness with the space surveillance telescope”, Applied Optics, Vol. 55, Issue 15, pp. 4036-4046, (2016).
[10]	Tyler Hardy, Stephen Cain, Jae Jeon, and Travis Blake, 2015 “Improving space domain awareness through unequal-cost multiple hypothesis testing in the space surveillance telescope”, Applied Optics, Vol. 54, Issue 17, pp. 5481-5494, (2015).
[11]	Nicholas J. Yielding, Stephen C. Cain, Michael D. Seal, 2018 "Statistical photo calibration of photodetectors for radiometry without calibrated light sources," Opt. Eng. Vol. 57 no. (1) DOI: 014107 (25 January 2018).
[12]	A. P. Dempster; N. M. Laird; D. B. Rubin, 1977 " Maximum Likelihood from Incomplete Data via the EM Algorithm", Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1. (1977), pp. 1-38.
[13]	Daniel A. LeMaster and Stephen C. Cain, "Multichannel blind deconvolution of polarimetric imagery," J. Opt. Soc. Am. A 25, 2170-2176 (2008).
[14]	Timothy J. Schulz, "Multiframe blind deconvolution of astronomical images," J. Opt. Soc. Am. A 10, 1064-1073 (1993).
[15]	L. A. Shepp and Y. Vardi, 1982 "Maximum Likelihood Reconstruction for Emission Tomography," in IEEE Transactions on Medical Imaging, vol. 1, no. 2, pp. 113-122, Oct. 1982.

Cite This Article

Plain Text BibTeX RIS

APA Style

Stephen Cain. (2021). Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. American Journal of Optics and Photonics, 9(1), 10-17. https://doi.org/10.11648/j.ajop.20210901.12

Copy | Download

ACS Style

Stephen Cain. Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. Am. J. Opt. Photonics 2021, 9(1), 10-17. doi: 10.11648/j.ajop.20210901.12

Copy | Download

AMA Style

Stephen Cain. Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. Am J Opt Photonics. 2021;9(1):10-17. doi: 10.11648/j.ajop.20210901.12

Copy | Download

@article{10.11648/j.ajop.20210901.12,
  author = {Stephen Cain},
  title = {Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration},
  journal = {American Journal of Optics and Photonics},
  volume = {9},
  number = {1},
  pages = {10-17},
  doi = {10.11648/j.ajop.20210901.12},
  url = {https://doi.org/10.11648/j.ajop.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajop.20210901.12},
  abstract = {This paper introduces a new space object detection algorithm that is designed to process image data taken from astronomical telescopes for the purposes of finding sources of optical radiation in space. Specifically, the algorithm is designed to find unresolvable space objects or objects that possess an angular size that is too small to appear as anything, but a point source as viewed through the telescope conducting the search. The proposed approach involves calibrating the image data into units of photoelectrons and then executing an estimation algorithm to compute the strength of the hypothetical sources in the image. A Likelihood Ratio Test (LRT) is then implemented to make a determination if the hypothetical sources are classified as space objects or not. The proposed algorithm is demonstrated to achieve a higher probability of detecting unresolvable objects than the matched filter, which is still the state-of-the-art approach for finding optical sources in astronomical images. The new approach involves a pre-processing step where the amplitude of the optical source in a given test location is estimated under the hypothesis that at optical source exists at that location. The median filter is used to estimate the background level in the vicinity of the test location. Once these parameters are estimated, A likelihood ratio test is used to determine whether an object is present at the test location. The new algorithm is tested against the matched filter detector using two sets of measured short exposure data of the star Polaris and two stars in its vicinity taken with a small telescope. Receiver Operating Characteristic (ROC) curves are produced for the two detection schemes showing that the new algorithm out-performs the old one with a difference of 10 percent in the probability of detection, which is demonstrated to be statistically significant in these experiments with confidence as high as 90%.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration
AU  - Stephen Cain
Y1  - 2021/03/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajop.20210901.12
DO  - 10.11648/j.ajop.20210901.12
T2  - American Journal of Optics and Photonics
JF  - American Journal of Optics and Photonics
JO  - American Journal of Optics and Photonics
SP  - 10
EP  - 17
PB  - Science Publishing Group
SN  - 2330-8494
UR  - https://doi.org/10.11648/j.ajop.20210901.12
AB  - This paper introduces a new space object detection algorithm that is designed to process image data taken from astronomical telescopes for the purposes of finding sources of optical radiation in space. Specifically, the algorithm is designed to find unresolvable space objects or objects that possess an angular size that is too small to appear as anything, but a point source as viewed through the telescope conducting the search. The proposed approach involves calibrating the image data into units of photoelectrons and then executing an estimation algorithm to compute the strength of the hypothetical sources in the image. A Likelihood Ratio Test (LRT) is then implemented to make a determination if the hypothetical sources are classified as space objects or not. The proposed algorithm is demonstrated to achieve a higher probability of detecting unresolvable objects than the matched filter, which is still the state-of-the-art approach for finding optical sources in astronomical images. The new approach involves a pre-processing step where the amplitude of the optical source in a given test location is estimated under the hypothesis that at optical source exists at that location. The median filter is used to estimate the background level in the vicinity of the test location. Once these parameters are estimated, A likelihood ratio test is used to determine whether an object is present at the test location. The new algorithm is tested against the matched filter detector using two sets of measured short exposure data of the star Polaris and two stars in its vicinity taken with a small telescope. Receiver Operating Characteristic (ROC) curves are produced for the two detection schemes showing that the new algorithm out-performs the old one with a difference of 10 percent in the probability of detection, which is demonstrated to be statistically significant in these experiments with confidence as high as 90%.
VL  - 9
IS  - 1
ER  -

Copy | Download

Author Information

Stephen Cain

Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson Air Force Base, Dayton, Ohio, USA

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Stephen Cain. (2021). Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. American Journal of Optics and Photonics, 9(1), 10-17. https://doi.org/10.11648/j.ajop.20210901.12

Copy | Download

ACS Style

Stephen Cain. Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. Am. J. Opt. Photonics 2021, 9(1), 10-17. doi: 10.11648/j.ajop.20210901.12

Copy | Download

AMA Style

Stephen Cain. Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration. Am J Opt Photonics. 2021;9(1):10-17. doi: 10.11648/j.ajop.20210901.12

Copy | Download

@article{10.11648/j.ajop.20210901.12,
  author = {Stephen Cain},
  title = {Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration},
  journal = {American Journal of Optics and Photonics},
  volume = {9},
  number = {1},
  pages = {10-17},
  doi = {10.11648/j.ajop.20210901.12},
  url = {https://doi.org/10.11648/j.ajop.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajop.20210901.12},
  abstract = {This paper introduces a new space object detection algorithm that is designed to process image data taken from astronomical telescopes for the purposes of finding sources of optical radiation in space. Specifically, the algorithm is designed to find unresolvable space objects or objects that possess an angular size that is too small to appear as anything, but a point source as viewed through the telescope conducting the search. The proposed approach involves calibrating the image data into units of photoelectrons and then executing an estimation algorithm to compute the strength of the hypothetical sources in the image. A Likelihood Ratio Test (LRT) is then implemented to make a determination if the hypothetical sources are classified as space objects or not. The proposed algorithm is demonstrated to achieve a higher probability of detecting unresolvable objects than the matched filter, which is still the state-of-the-art approach for finding optical sources in astronomical images. The new approach involves a pre-processing step where the amplitude of the optical source in a given test location is estimated under the hypothesis that at optical source exists at that location. The median filter is used to estimate the background level in the vicinity of the test location. Once these parameters are estimated, A likelihood ratio test is used to determine whether an object is present at the test location. The new algorithm is tested against the matched filter detector using two sets of measured short exposure data of the star Polaris and two stars in its vicinity taken with a small telescope. Receiver Operating Characteristic (ROC) curves are produced for the two detection schemes showing that the new algorithm out-performs the old one with a difference of 10 percent in the probability of detection, which is demonstrated to be statistically significant in these experiments with confidence as high as 90%.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Space Object Detection Algorithm Leveraging Absolute Photo-Detector Calibration
AU  - Stephen Cain
Y1  - 2021/03/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajop.20210901.12
DO  - 10.11648/j.ajop.20210901.12
T2  - American Journal of Optics and Photonics
JF  - American Journal of Optics and Photonics
JO  - American Journal of Optics and Photonics
SP  - 10
EP  - 17
PB  - Science Publishing Group
SN  - 2330-8494
UR  - https://doi.org/10.11648/j.ajop.20210901.12
AB  - This paper introduces a new space object detection algorithm that is designed to process image data taken from astronomical telescopes for the purposes of finding sources of optical radiation in space. Specifically, the algorithm is designed to find unresolvable space objects or objects that possess an angular size that is too small to appear as anything, but a point source as viewed through the telescope conducting the search. The proposed approach involves calibrating the image data into units of photoelectrons and then executing an estimation algorithm to compute the strength of the hypothetical sources in the image. A Likelihood Ratio Test (LRT) is then implemented to make a determination if the hypothetical sources are classified as space objects or not. The proposed algorithm is demonstrated to achieve a higher probability of detecting unresolvable objects than the matched filter, which is still the state-of-the-art approach for finding optical sources in astronomical images. The new approach involves a pre-processing step where the amplitude of the optical source in a given test location is estimated under the hypothesis that at optical source exists at that location. The median filter is used to estimate the background level in the vicinity of the test location. Once these parameters are estimated, A likelihood ratio test is used to determine whether an object is present at the test location. The new algorithm is tested against the matched filter detector using two sets of measured short exposure data of the star Polaris and two stars in its vicinity taken with a small telescope. Receiver Operating Characteristic (ROC) curves are produced for the two detection schemes showing that the new algorithm out-performs the old one with a difference of 10 percent in the probability of detection, which is demonstrated to be statistically significant in these experiments with confidence as high as 90%.
VL  - 9
IS  - 1
ER  -

Copy | Download