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Development of a very faint meteor detection system based on an EMCCD sensor and matched filter processing

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Abstract

The mass ranges of meteors, imaged by electro-optical (EO) cameras and backscatter radar receivers, for the most part do not overlap. Typical EO systems detect meteoroid masses down to 10− 5 kg or roughly magnitude + 2 meteors when using moderate field of view optics, un-intensified optical components, and meteor entry velocities around 45 km/sec. This is near the high end of the mass range of typical meteor radar observations. Having the same mass meteor measured by different sensor wavelength bands would be a benefit in terms of calibrating mass estimations for both EO and radar. To that end, the University of Western Ontario (UWO) has acquired and deployed a very low light imaging system based on an electron-multiplying CCD camera technology. This embeds a very low noise, per pixel intensifier chip in a cooled camera setup with various options for frame rate, region of interest and binning. The EO system of optics and sensor was optimally configured to collect 32 frames per second in a square field of view 14.7 degrees on a side, achieving a single-frame stellar limiting magnitude of mG = + 10.5. The system typically observes meteors of + 6.5. Given this hardware configuration, we successfully met the challenges associated with the development of robust image processing algorithms, resulting in a new end-to-end processing pipeline now in operation since 2017. A key development in this pipeline has been the first true application of matched filter processing to process the faintest meteors possible in the EMCCD system while also yielding high quality automated metric measurements of meteor focal plane positions. With pairs of EMCCD systems deployed at two sites, triangulation and high accuracy orbits are one of the many products being generated by this system. These measurements will be coupled to observations from the Canadian Meteor Orbit Radar (CMOR) used for meteor plasma characterization and the Canadian Automated Meteor Observatory (CAMO) high resolution mirror tracking system.

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Acknowledgements

The authors thank Dr. William Cooke, lead of NASA’s Meteoroid Environment Office, for technical discussions and comments. Funding for this work was provided in part through NASA co-operative agreement 80NSSC21M0073. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada Discovery Grants program (Grants no. RGPIN-2016-04433) and the Canada Research Chairs program.

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  1. Gural Software and Analysis LLC, Lovettsville, VA, 20180, USA

    P. Gural

  2. Department of Physics and Astronomy and Institute for Earth and Space Exploration, University of Western Ontario, London, ON, N6A 3K7, Canada

    T. Mills, M. Mazur & P. Brown

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  1. P. Gural
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Contributions

All authors contributed to the original equipment design, ongoing data collection operations and daily analysis of measurements. Material preparation and analysis for this paper were performed by Peter S. Gural with much of the bulk post-detection processing done by Tristan Mills. The first draft of the manuscript was written by Peter S. Gural, with the science applicability reviewed and edited by Peter B. Brown. All authors commented on previous versions of the manuscript and have read and approved the final manuscript.

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Correspondence to P. Gural.

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The authors have no conflicts of interest to declare that are relevant to the content of this article. There was no reasearch that involved humans or animals. Funding sources have been disclosed in the Acknowledgements section. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.The authors have no relevant non-financial interests to disclose.

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Appendices

Appendix A: Summary of algorithm methodologies

A comparison of potential algorithms that could be used at various stages of the processing pipeline are summarized in Table 1. This is by no means an exhaustive review of possible processing algorithms, but is fairly representative of methods used in a variety of legacy meteor detection applications developed over the past two decades and applied to video imagery.

Table 1 The pros and cons of various processing pipeline algorithms
Full size table

Note 1 - The Hough space orientation angle can be estimated at an exceedance pixel by several alternative approaches to the standard accumulation over all angles Hough transform [15]. For example, one can use 1) a phase coded disk applied to a binary exceedance image [10], 2) a Hueckel transform applied to a gray scale image [26], 3) pixel pairing of local neighborhood exceedance pixels (Gural 1999a), or 4) linked exceedances clustered into line segments with a covariance eigenvalues estimation.

Appendix B: Configuration parameter list

The following lists the file contents of the configuration parameters used for the EMCCD detection application.

Read version = 2.11

Sample time/frame in sec = 0.031119

Duty factor = 1.0

Magnitude zero point = 16.72

Interleave type = 0

Backgnd low cutoff (%) = 5

Backgnd high cutoff (%) = 50

Number compressed frames M = 64

Max downscaling 1,2,4,8,16 = 2

Primary sigma factor = 0.0

Cell block size (pixels) = 32

Max clusters per frame = 20

Neighbors + self tuplet = 5

Tiny variance = 0.010000

Saturation gray level = 35535.000000

Cluster sigma factor = 1.5

Cluster detection (dB) = -2.0

Tracker FIRM = 3 of 4

Tracker CLOSED = 2 of 3

Tracker TROUBLED = 1

Tracker max tracklets = 200

Tracker max history = 100

MFIL accel model -1,0,+ 1 = 0

MFIL accel nframes limit1 = 10

MFIL accel nframes limit2 = 15

Detect Vmin (pixels/frame) = 2.000000

Detect Vmax (pixels/frame) = 35.000000

Detect linearity (pixels) = 2.000000

Detect model fit (pixels) = 3.000000

Min #frames for detect = 3

#frames to extend track = 8

SNR to cull extended frms = 1.0

PSF size (pixels) = 3

PSF halfwidth (pixels) = 1.130000

Dedupe angle limit (deg) = 10.0

Dedupe rel velocity % = 40.0

Dedupe max distance (pixels) = 10.0

TRT Threshold = 0.6

MLE Threashold (dB) = 0.0

Reporting option = 28

Parameters that can influence sensitivity of detection:

  1. 1.

    Number of compressed frames (64 = 2.16 sigma; 44 = 2.0 sigma; 32 = 1.87 sigma)

  2. 2.

    Neighbors + self tuplet cluster size (nominally 6)

  3. 3.

    Sigma factor for cluster detection (nominally 2.0)

  4. 4.

    SNR for cluster detection (nominally 0.0)

  5. 5.

    MLE threshold (nominally 0 dB, but 8-10 dB could be used to screen out faint false alarms but also potentially faint meteors)

Reporting option is the sum of the following:

1 = Cluster/tracker detections (no dedupe)

2 = Matched filter detections (no dedupe)

4 = Matched filter detections (de-duplicated)

8 = Matched filter detect BMPs (no dedupe)

16 = ASGARD formatted file of MF detections (deduped)

Max downscaling option to aid blob detection by reducing resolution which includes 1x1, 2x2, 4x4, 8x8, 16x16 up to Max x Max specified

Examples:

  • 1 = 1x1 only (no downscaling single processing path)

  • 2 or 3 = 1x1 and 2x2 downscaling processing paths

  • 4, 5, 6 or 7 = 1x1, 2x2 and 4x4 downscaling processing paths

  • 8 to 15 = 1x1, 2x2, 4x4 and 8x8 downscaling processing paths

  • >= 16 uses 1x1, 2x2, 4x4, 8x8 and 16x16 downscaling paths

Match filter acceleration model options for refinement:

-1 = Zero acceleration and zero jerkiness

0 = Constant acceleration and zero jerkiness

1 = Linear acceleration and constant jerkiness

NOTE: This is a default value which gets adjusted down for shorter pick count meteors as follows:

< nframes limit1 picks enforces no higher than zero accel

< nframes limit2 picks enforces no higher than constant accel

Set limit1 and limit2 to zero to force only one accel model.

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Gural, P., Mills, T., Mazur, M. et al. Development of a very faint meteor detection system based on an EMCCD sensor and matched filter processing. Exp Astron 53, 1085–1126 (2022). https://doi.org/10.1007/s10686-021-09828-3

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