dragonfleye: a passive approach to aerial collision sensing

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DRAGONFLEYE: A PASSIVE APPROACH TO AERIAL
COLLISION SENSING
CYRUS MINWALLA
A DISSERTATION SUBMITTED TO THE FACULTY OF GRADUATE
STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF ,
DOCTOR OF PHILOSOPHY
GRADUATE PROGRAM IN COMPUTER SCIENCE AND ENGINEERING
YORK UNIVERSITY
TORONTO, ONTARIO
APRIL 2013
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DRAGONFLEYE: A PASSIVE APPROACH
TO AERIAL COLLISION SENSING
by Cyrus Minwalla
a dissertation submitted to the Faculty of Graduate Stud
ies of York University in partial fulfilment of the require
ments for the degree of
DOCTOR OF PHILOSOPHY
© 2013
Permission has been granted to: a) YORK UNIVER
SITY LIBRARIES to lend or sell copies of this disserta
tion in paper, microform or electronic formats, and b) LI
BRARY AND ARCHIVES CANADA to reproduce, lend,
distribute, or sell copies of this dissertation anywhere in
the wodd in microform, paper or electronic formats and
to authorise or procure the reproduction, loan, distribu
tion or sale of copies of this dissertation anywhere in the
world in microform, paper or electronic formats.
The author reserves other publication rights, and nei
ther the dissertation nor ~xtensive extracts for it may
be printed or otherwise reproduced without the author's
written permission.
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DRAGONFLEYE: A PASSIVE APPROACH TO AERIAL
COLLISION SENSING
by Cyrus Minwalla
By virtue of submitting this document electronically, the author certifies that this
is a true electronic equivalent of the copy of the dissertation approved by York
University for the award of the degree. No alteration of the content has occurred
and if there are any minor variations in formatting, they are as a result of the
conversion to Adobe Acrobat format (or similar software application).
Examination Committee Members:
1. Dr. Richard Hornsey
2. Dr. Robert Allison
3. Dr. Mokhtar Aboelaze
4. Dr. Burton Ma
5. Dr. Costas Armenakis
6. Dr. Siu O'Young
Abstract
This dissertation describes the design, development and test of a passive wide-field
qptical aircraft collision sensing instrument titled 'Dragonfl.EYE'. Such a "sense
and-avoid" instrument is desired for autonomous unmanned aerial systems oper
ating in civilian airspace. The instrument was configured as a network of smart
camera nodes and implemented using commercial, off-the-shelf components. An
end-to-end imaging train model was developed and important figures of merit were
derived. Transfer functions arising from intermediate mediums were discussed and
their impact assessed. Multiple prototypes were developed. The expected perfor
mance of the instrument was iteratively evaluated on the prototypes, beginning
with modeling activities followed by laboratory tests, ground tests and flight tests.
A prototype was mounted on a Bell 205 helicopter for flight tests, with a Bell 206
helicopter acting as the target. Raw imagery was recorded alongside ancillary air-.
craft data, and stored for the offiine ass_essment of performance. The "range at first
detection" (Ro), is presented as a robust measure of sensor performance, based on
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a suitably defined signal-to-noise ratio. The analysis treats target radiance fluc
tuations, ground clutter, atmospheric effects, platform motion and random noise
elements. Under the measurement conditions, R exceeded flight crew acquisition
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ranges. Secondary figures of merit are also discussed, including time to impact, tar
get size and growth, and the impact of resolution on detection range. The hardware
was structured to facilitate a real-time hierarchical image-processing pipeline, with
selected image processing techniques introduced. In particular, the height of an
observed event above the horizon compensates for angular motion of the helicopter
platform.
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.
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Acknowledgements
The hardware implementation of the DragonfiEYE sensor developed in this
dissertation required the participation of technical staff at the National Research
Council of Canada and York University, and from students, summer and otherwise,
under my supervision at York University and NRC Canada. I; therefore, gratefully
acknowledge the work of the following individuals: Paris Ang (NRC-Carleton),
Stanley Lio (VISOR), Kyle Watters (VISOR), Gary Zeng (VISOR), Sepehr Nazari
(VISOR), Jeremy Chan-Hao Wang (VISOR), Scott Desjardins (NRC), Michael
Mullins (NRC), James Jamison (NRC), Camille Lebrun (NRC), Malcolm Imray
(NRC), Scott Desjardins (NRC), Ator Sarkisoff (Petrie) and Paul Woitalla (Petrie).
I would like to extend thanks to colleagues Mussie Tekeste, Neela Maharaj and
Michael Liscombe for their general contributions to camera characterization.
This thesis would not have been possible without the tireless efforts of Dr.
Richard Homsey and Dr. Paul Thomas at the Vision Sensor Laboratory, whose
wisdom, guidance and insight have been invaluable throughout this journey. For
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that I thank them.
My supervisors at NRC Canada, Sion Jennings and Kristopher Ellis, deserve
thanks for their efforts. In particular, I would like to thank Si on Jennings for being
an amazing manager, and Kristopher Ellis, for being one of the smartest people I
know. I would like to thank the pilots, Robert Erdos and Stephan Carignan for
their expert flying under such dangerous conditions, and Dr. Gregory Craig for
being my chaperone in the skies.
Thanks go out to the senior colleagues at VISOR, including newly minted Drs.
Edward Shen, Wei Gao, Elliot Tsai and Christopher Thomas, for making the lab
oratory environment a happy (and fun!) place to be.
Last of all, I would like to thank my wife, Ne ha, for her unwavering faith in my
abilities, and my son, Michael, for being the impetus to pen this dissertation post
haste. I love you both.
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Table of Contents
Abstract iv
Acknowledgements vi
Table of Contents viii
List of Tables xix
List of Figures
XXI
Abbreviations xxx
1 Introduction 1
1.1 Synopsis . 1
1. 2 General Discussion 1
1.3 Definition of sense-and-avoid (SAA) . 2
1.4 Background and Motivation . . . . . 3
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1.5 Active systems ............. . 5
1.5.1 Identification Friend-or-Foe (IFF) 6
1.5.2 · Microwave radar .. 7
1.5.3 Airborne laser radar 8
1.6 Passive sensing ...... . 9
1.6.1 Passive .optical imaging systems 9
1.6.2 Visible, near-IR sensing 10
1.6.3 Infrared sensing 12
1. 7 Contributions 13
1.8 Conclusion . . 15
1.9 Organization 16
2 Problem Statement 17
2.1 Synopsis ... 17
2.2 Introduction . 17
2.3 Collision detection paradigm . 18
2.3.1 Rationale .... 19
2.3.2 General scenario 20
2.3.3 Instrument concept 21
2.4 Constraints . . . . . . . . 23
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2.4.1 Minimum miss distance and angular coverage 23
2.4.2 Time-to-evade metric . 24
2.4.3 Collision geometry and sensor field-of-view 25
2.4.4 Heading and angular target size 27
2.4.5 Angular resolution and total field coverage 28
2.5 Smart camera . 30
2.5.1 History. 31
2.5.2 Cellphone cameras 32
2.5.3 The 'compression ratio' figure of merit 33
2.6 Embedded computing. 36
2.6.1 History ..... 36
2.6.2 The OMAP APU Architecture . 37
2. 7 Tests 41
2.7.1 Modeling
2.7.2 Laboratory tests 42
2.7.3 Field tests . . . . 43
2.8 End-point requirements . 45
2.9 Conclusion . . . . . . . . 45
3 Scene Models 47
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