UAS Collision Warning and Passive Sensor Fusion Algorithms for Multiple Acoustic Transient

205 Pages·2017·5.61 MB·English

by  Wenbo Dou

University of Connecticut
OpenCommons@UConn
Doctoral Dissertations University of Connecticut Graduate School
5-23-2017
UAS Collision Warning and Passive Sensor Fusion
Algorithms for Multiple Acoustic Transient Emitter
Localization
Wenbo Dou
[email protected]
Follow this and additional works at:https://opencommons.uconn.edu/dissertations
Recommended Citation
Dou, Wenbo, "UAS Collision Warning and Passive Sensor Fusion Algorithms for Multiple Acoustic Transient Emitter Localization"
(2017).Doctoral Dissertations. 1425.
https://opencommons.uconn.edu/dissertations/1425
UAS Collision Warning and Passive Sensor
Fusion Algorithms for Multiple Acoustic
Transient Emitter Localization
WenboDou,Ph.D.
UniversityofConnecticut,2017
ABSTRACT
This dissertation considers two important topics in the area of estimation, target track-
ing and sensor fusion. The first topic is closest point of approach (CPA) prediction for
unmanned aircraftsystems (UAS) collisionwarning and thesecond topic ispassive sensor
fusionformultipleacoustictransientemitterlocalization.
To operate within a controlled airspace, UAS must have the capability to sense and
avoid collisions with non-cooperative aircraft. This dissertation presents an inexpensive
system design and develops an algorithm for estimating the CPA between the ownship
and the intruder and a collision warning scheme using only bistatic range and range rate
measurementsfromamultistaticradar.
Since it is vital for soldiers to be able to accurately localize sources of hostile fire in
the battlefield for situational awareness and threat assessment, this dissertation develops
bothcentralizedanddistributedpassivesensorfusionalgorithmstoaccuratelyestimatethe
number of acoustic transient emitters and their locations using bearing and time of arrival
measurements.
UAS Collision Warning and Passive Sensor
Fusion Algorithms for Multiple Acoustic
Transient Emitter Localization
WenboDou
B.E.,NanyangTechnologicalUniversity,Singapore,2011
ADissertation
SubmittedinPartialFulfillmentofthe
RequirementsfortheDegreeof
DoctorofPhilosophy
atthe
UniversityofConnecticut
2017
Copyrightby
WenboDou
2017
ii
APPROVAL PAGE
DoctorofPhilosophyDissertation
UAS Collision Warning and Passive Sensor
Fusion Algorithms for Multiple Acoustic
Transient Emitter Localization
Presentedby
WenboDou,B.E.EE.
MajorAdvisor
YaakovBar-Shalom
AssociateAdvisor
PeterK.Willett
AssociateAdvisor
KrishnaR.Pattipati
UniversityofConnecticut
2017
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ACKNOWLEDGMENTS
First of all, I want to thank Professor Yaakov Bar-Shalom for providing me such a
priceless opportunity to pursue my Ph.D. degree under the supervision of (without “one
of”) the greatest researcher in the target tracking field. I feel honored to be his 38-th
Ph.D.student. HiscontinuousguidanceandsupportmakemyPh.D.journeyenjoyableand
unforgettable. I remember that when he offered me the Ph.D. opportunity, I did not even
know random variables. Now I have mastered some of the state-of-the-art technologies to
tackle practical challenging tracking problems. Not only do I benefit from his expertise
in research, his serious attitude and his organized and systematic manner of conducting
researchaswellasinotheractivitiesisalsoavaluableassetformyfuture.
Next,IwouldliketothankProfessorPeterWillett,oneofmyassociateadvisors,forhis
kind helpduring the lastfew years. He has contributedto a lotof helpful commentsto im-
provemypapers. IenjoyedhiscommentsalotbecauseIcannotonlylearntheappropriate
way of writing technical articles but always also learn new English from a native speaker.
Heisagreatspeakertolistentointheclassroom,inatechnicalseminar(Youcanverifythis
bywatchingacoupleofhislecturesonYouTube)orinacasualmeeting. Ihadagreattime
in his advanced signal processing class where I officially learned particle filtering for the
firsttime. HeprovidedmetheopportunitytoserveasthestudentassociateeditorforIEEE
AerospaceandElectronicSystemsMagazine,whichenrichesmyprofessionalexperience.
I am also grateful to Professor Krishna Pattipati, my other associate advisor. I took
three, the most from any professor, courses from him during my graduate study. I enjoyed
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histeachinginclassandfullymasteredtheknowledgethroughhishomeworkquestionsand
computer assignments. His neural network and data mining classes offer me the chance
to learn the field of machine learning right in this artificial intelligence era. His linear
programmingclassprovidesmewithabetterunderstandingofassignmentproblems,which
isthefoundationofthedataassociationproblempresentedinthisdissertation. Heprovides
me a chance to work on research problems using machine learning techniques, which help
alotinmyjobsearch.
It is my great honor to work with the above three UCONN ECE System rockstar pro-
fessors for my research. My sincere thanks also go to Professor Robert Lynch, Professor
PeterLuh,ProfessorShengliZhouand Professor ShalabhGuptafromDepartmentofECE
and Professor Elizabeth Schifano from Department of Statistics for their time and help in
teachingmetheknowledgerequiredtocompletemydissertation.
I also want to extend my thanks to Lance Kaplan and Jemin George for hosting me at
the US Army Research Lab in the summer of 2014. They have been offering continuous
guidance and feedback to my work on the multiple transient emitter localization problem
fromthebeginning.
I would like to thank my best friend Zhiheng Xu and his wife Bing Yan, who not only
helppassmyResumetomyadvisorandbutalsomakemyadaptiontolifeinUnitedStates
sosimple.
I want to thank my current and past labmates Qin Lu, Kaipei Yang, Radu Visina,
Katherine Domrese, Xin Zhang, Shuo Zhang, Richard Osborne, Xiufeng Song, Ramona
Georgescu, Sora Choi, Ting Yuan, Kevin Romeo and Djedjiga Belfadel; fellow students
Yaowen Yu, Weiji Han, Yujia Li, Danxu Zhang, Ashwin Billava, Adam Markman and
Pujitha Mannaru; colleagues Xiao Xiao, Marcus Baum, Karl Granstrom and Balakumar
Balasingamforhelpingandworkingwithme.
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IwouldliketothankmygrandfathersGuangshunDouandChuanpuJiang,myparents
Chunshan Dou and Longhua Jiang, my girlfriend Xiaoyan Tang and all the other family
membersfortheirloveandsupport.
Last but not least, I want to thank the financial support by ARO Grant W991NF-10-1-
0369.
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Contents
Ch.1. Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 UAScollisionwarning . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Multipleacoustictransientemitterlocalization . . . . . . . . . . . 3
1.1.3 Listofpublicationstodate . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Literaturereview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Relatedcollisionwarningalgorithms . . . . . . . . . . . . . . . . . 6
1.2.2 Relateddataassociationalgorithms . . . . . . . . . . . . . . . . . 7
1.3 ContributionandMethodology . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3.1 UAScollisionwarning . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3.2 Multipleacoustictransientemitterlocalization . . . . . . . . . . . 12
Ch.2. Bistatic Measurement Fusion from Multistatic Configurations for Air
CollisionWarning 15
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1 ParameterObservability . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 ConfidenceRegionintheGeneralCase . . . . . . . . . . . . . . . 24
2.2.3 ConfidenceRegionWhenIntruderandOwnshipatSameAltitude . 26
2.3 ScenariosandObservabilityAnalysis . . . . . . . . . . . . . . . . . . . . . 27
2.4 TheMaximumLikelihoodEstimator . . . . . . . . . . . . . . . . . . . . . 33
2.5 CollisionWarningApproaches . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.1 Collision Warning via Hypothesis Testing Based on a Generalized
LikelihoodFunction . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.2 CollisionWarningBasedonaBayesianApproach . . . . . . . . . . 40
2.6 SimulationResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.6.1 EfficiencyofMLEstimatoroftheTargetParameter . . . . . . . . . 43
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2.6.2 EfficiencyoftheCPATimeEstimate . . . . . . . . . . . . . . . . . 45
2.6.3 CollisionWarningBasedontheGeneralizedLikelihoodFunction . 48
2.6.4 CollisionWarningBasedontheBayesianApproach . . . . . . . . . 55
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Ch.3. Evaluation of Fusion Algorithms for Passive Localization of Multiple
TransientEmitters 59
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2 ProblemDescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3 TheS-Dassignmentalgorithm . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.2 TheoptimizationviaLagrangianrelaxation . . . . . . . . . . . . . 70
3.3.3 Thesequentialm-best2-Dassignmentalgorithm . . . . . . . . . . 72
3.4 Uniform-GaussianMixture(UGM)Formulation . . . . . . . . . . . . . . . 72
3.4.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.4.2 TheExpectation-MaximizationAlgorithm . . . . . . . . . . . . . . 77
3.4.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
3.4.4 UseoftheInformationCriterionforCardinalitySelection . . . . . . 83
3.5 PoissonPointProcess(PPP)Model . . . . . . . . . . . . . . . . . . . . . . 84
3.5.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.5.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.5.3 UseoftheInformationCriterionforCardinalitySelection . . . . . . 91
3.6 SimulationResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.6.1 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.6.2 Performancemetrics . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.6.3 Assignmentalgorithms . . . . . . . . . . . . . . . . . . . . . . . . 95
3.6.4 EM-basedalgorithms . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.6.5 AssignmentalgorithmsandEM-basedalgorithms . . . . . . . . . . 109
3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Ch.4. DistributedFusionAlgorithmforPassiveLocalizationofMultipleTran-
sientEmitters 115
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
4.1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
4.1.4 ChapterOrganization . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.2.1 GraphModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
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