Planning Methods for Aerial Exploration and Ground Target Tracking

203 Pages·2009·2.85 MB·English

Linköpingstudiesinscienceandtechnology. Thesis.
No.1420
Planning Methods for
Aerial Exploration and
Ground Target Tracking
Per Skoglar
REGLERTEKNIK
AUTOMATIC CON T R O L
LINKÖPING
DivisionofAutomaticControl
DepartmentofElectricalEngineering
LinköpingUniversity,SE-58183Linköping,Sweden
http://www.control.isy.liu.se
[email protected]
Linköping2009
ThisisaSwedishLicentiate’sThesis.
SwedishpostgraduateeducationleadstoaDoctor’sdegreeand/oraLicentiate’sdegree.
ADoctor’sDegreecomprises240ECTScredits(4yearsoffull-timestudies).
ALicentiate’sdegreecomprises120ECTScredits,
ofwhichatleast60ECTScreditsconstituteaLicentiate’sthesis.
Linköpingstudiesinscienceandtechnology. Thesis.
No.1420
PlanningMethodsforAerialExplorationandGroundTargetTracking
PerSkoglar
[email protected]
www.control.isy.liu.se
DepartmentofElectricalEngineering
LinköpingUniversity
SE-58183Linköping
Sweden
ISBN978-91-7393-509-8 ISSN0280-7971 LiU-TEK-LIC-2009:28
Copyright(cid:13)c 2009PerSkoglar
PrintedbyLiU-Tryck,Linköping,Sweden2009
TillMaria

Abstract
This thesis considers unmanned airborne surveillance systems equipped with electro-
optical vision sensors. The aim is to increase the level of autonomy and improve the
system performance by the use of planning methods for aerial exploration and target
tracking. Thegeneralproblemisverycomplexduetothe“curse-of-dimensionality”and
suboptimalapproachesarenecessaryinordertohandleadvancedsurveillancemissions.
A general planning framework is proposed and the planner contains a high-level sched-
uler and a number of planning modes. Each mode consists of planning modules that
solve smaller sub-tasks and in this thesis a number of these modules are developed. In
particular, two major approaches are treated; information based planning, and Bayesian
targetsearch. Inaddition,theon-roadtargettrackingproblemistreatedindetailandan
algorithmbasedontheParticlefilterispresented.
In information based planning, different information measures are used to solve the
optimal trajectory planning problem for bearings-only estimation. Thus, the problem is
howtomaneuveranunmannedaerialvehicle(UAV)toachievethebestpossibleestimate
ofatargetlocationwhileobservingitwithavisionsensor. ApproachesbasedontheIn-
formationfilterandthedifferentialentropyareproposed. TheInformationfilterapproach
isalsousedtodevelopanexplorationframeworkwheretheUAVflighttrajectoryandthe
sensorpointingdirectionareconsideredconcurrently.
In Bayesian target search, the aim is to find a target as quickly as possible given
somepriorknowledgeofwhereitmightbe. Methodsbasedonbothgradientsearchand
combinatorialoptimizationroutinesareproposedforthesearchproblemwhereaUAVis
equippedwithacontrollablevisionsensorwithlimitedfield-of-view.
v

Acknowledgments
FirstofallIwouldliketothankmysupervisorProf.FredrikGustafssonforthesupport
andguidanceduringmyyearssofaratAutomaticControl(RT),aspectaculargroupunder
theleadershipofProf.LennartLjung. IamverygratefultoLennartandFredrik,andalso
totheprojectleaderofARCUSMorganUlvkloandtheformerheadofSensorInformatics
Dr.JörgenAhlbergatFOI,formakingthetransferfromFOItoRTpossible.
TwomajorreasonsfortheexistenceofthisthesisareDr.UmutOrgunerandDr.David
Törnqvist, my valuable co-authors, proofreaders and discussion partners. Who needs
BatmanandRobinwhenyouareintown!? Thankyouforalltheencouragementandthe
patience! SomeoftheideasinthisthesisaroseduringmytimeatFOIandthediscussions
with,inparticular,Dr. JonasNygårds. YouhaveanimpressiveflowofideasandIenjoy
discussingwithyou.
A large number of people have made contributions to this work. I am grateful to
all the people in ARCUS (can not mention them all...), the co-authors at FOI (Morgan
Ulvklo, Dr. Jonas Nygårds, Dr. Jörgen Karlholm, Dr. Patrik Hermansson, Dr. Petter
Ögren, Dr. Johan Hamberg and Rikard Björström), and the MSSLab/ARCUS people at
FOI (Dr. David Lindgren, Dr. Christina Grönwall, Fredrik Näsström, Mikael Karlsson,
Gustav Haapalahti, Mirsad Cirkic, Dr. Joakim Rydell and Lic. Karl-Göran Stenborg). I
wouldalsoliketothankLic.HenrikTidefeltandDr.GustafHendeby, forthehelpwith
variouslayoutproblems,andUllaSalaneck,RT,andAgnetaAhlström,FOI,forsupport
withvariousadministrationissues.
ThisworkisapartoftheTAIS-programmanagedbyFMV(SwedishDefenceMateriel
Administration)andfinancedbytheSwedishArmedForcesandVinnova(TheSwedish
Governmental Agency for Innovation Systems), which are hereby gratefully acknowl-
edged.
Finally,IwouldliketothankmywifeMaria. Thankyouforallthelove,patienceand
supportyouaregivingme!
Linköping,October2009
PerSkoglar
vii

Contents
1 Introduction 1
1.1 ARCUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 UAVSurveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
I Background 9
2 PlanningforUAVSurveillance 11
2.1 DataFusionandSensorManagement . . . . . . . . . . . . . . . . . . . 12
2.1.1 TargetTracking . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.2 SensorManagement . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 OptimizationbasedPlanning . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 ThePlanningGoal-Whatisgoodplanning? . . . . . . . . . . . 14
2.2.2 PlanningConstraints . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.3 ScopeoftheThesis . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 StochasticOptimalControl . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 FiniteHorizonStochasticOptimalControl . . . . . . . . . . . . 16
2.3.2 ThePrincipleofOptimalityandtheDPAlgorithm . . . . . . . . 17
2.4 SuboptimalPlanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1 SuboptimalApproaches . . . . . . . . . . . . . . . . . . . . . . 17
2.4.2 PlanningFramework . . . . . . . . . . . . . . . . . . . . . . . . 19
3 GroundTargetTracking 23
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.1 TargetTracking . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.2 Multi-TargetTracking . . . . . . . . . . . . . . . . . . . . . . . 24
ix
x Contents
3.1.3 GroundTargetTracking . . . . . . . . . . . . . . . . . . . . . . 24
3.1.4 Electro-OpticalImagingSystemPerformance . . . . . . . . . . . 26
3.2 VisionSensorModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.1 Bearings-onlyObservationModel . . . . . . . . . . . . . . . . . 27
3.3 TargetMotionModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1 RandomWalkModel . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2 ConstantVelocityMotionModel . . . . . . . . . . . . . . . . . . 28
3.3.3 CoordinatedTurnModel . . . . . . . . . . . . . . . . . . . . . . 28
3.3.4 RoadNetworkModel. . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.5 RoadTargetMotionModel . . . . . . . . . . . . . . . . . . . . . 29
3.3.6 RoadTargetMotionModelwithmodifiedSpeedState . . . . . . 30
3.4 TargetTrackingFilters . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4.1 TheGeneralEstimationSolution . . . . . . . . . . . . . . . . . . 31
3.4.2 ParticleFilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4.3 Rao-BlackwellizedParticleFilter(RBPF) . . . . . . . . . . . . . 32
3.5 ARoadTargetFilterbasedonRBPF . . . . . . . . . . . . . . . . . . . . 33
3.6 ProbabilityofDetectionandNegativeInformation. . . . . . . . . . . . . 35
3.6.1 InformationfromNon-Detections . . . . . . . . . . . . . . . . . 35
3.6.2 ProbabilityofDetectionModel . . . . . . . . . . . . . . . . . . 36
3.7 SimulationResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.A FilterAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.A.1 KalmanFilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.A.2 ExtendedKalmanFilter . . . . . . . . . . . . . . . . . . . . . . 42
3.A.3 ParticleFilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.A.4 BayesianBootstrapParticleFilter . . . . . . . . . . . . . . . . . 45
3.A.5 Rao-BlackwellizedParticleFilter . . . . . . . . . . . . . . . . . 45
II InformationBasedPlanning 47
4 ObserverTrajectoryPlanningbasedonInformationFilter 49
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 ProblemDefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 SuboptimalApproaches . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 CertaintyEquivalenceControl . . . . . . . . . . . . . . . . . . . 52
4.3.2 Open-loopFeedbackControl . . . . . . . . . . . . . . . . . . . . 52
4.4 InformationMeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4.1 EntropicInformation . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4.2 MutualInformation . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4.3 TheInformationFilter . . . . . . . . . . . . . . . . . . . . . . . 54
4.5 CECPlannerbasedontheInformationFilter. . . . . . . . . . . . . . . . 55
4.6 OLFCPlannerbasedontheInformationFilter . . . . . . . . . . . . . . . 56
4.7 SimulationResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.7.1 CEC-EIFPlannerwithRandomWalkTargetin2D . . . . . . . . 58
4.7.2 OLFC-EIFPlannerwithRandomWalkTargetin2D . . . . . . . 59
4.7.3 CEC-EIFPlannerwithRandomWalkTargetin3D . . . . . . . . 60