optimal sensor management technique for an unmanned aerial vehicle tracking multiple mobile ground...
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Optimal Sensor Management Technique For An Unmanned Aerial Vehicle Tracking Multiple Mobile Ground TargetsNegar Farmani, Liang Sun, Daniel PackUnmanned System LabThe University of Texas at San Antonio
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Outline:IntroductionProblem Statement Optimal Sensor ManagementCoordinates TransformationsGimbal Pose SelectionExperimental ResultsConclusion
IntroductionTracking multiple mobile targets in optimal manner
Previous works
Novel optimal sensor management method
Increasing number of applications of UAVs- there is lack of techniques to track multiple targets in some optimal manner when the resources (sensors) are limited to fully carry out theMissionusing a Recursive Least Square filterA* method
Camera, limitations of the resolution, the range, and the field of view (FOV)Objective of paper: present optimal technique to manage a sensor to track multiple mobile targets3
Outline:IntroductionProblem Statement Sensor ManagementExperimental ResultsConclusion
sample scenario
Specifications:assume targets move on ground randomly.UAV : constant altitude and constant velocity Control input: bank angles Camera: Limited FOV random noise. The objectives: geo-localize ground targets minimize the error of estimation on ground.
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Outline:IntroductionProblem Statement Sensor ManagementExperimental ResultsConclusion
UAV And Target Relation
Associated Coordinate FramesThree frame of interest:Body Frame = (ib , jb , kb)Gimbal Frame= (ig , jg , kg) Camera Frame= (ic , jc , kc)
Three frame of intrest8
Target Projection
Target project into camera frame and translate to pixel locations(ex,ey)Gimbal pointing direction is determined by aligning optical axis of camera to desired direction9
Target Geo-Localization
Estimation Technique
Extended Kalman Filter (EKF)Motion model:
State of target:
Measurement model:
Geo-localizationExtended Kalman Filter (EKF): Prediction Step:
where
Geo-localization Measurement Update:
where
Candidate Generation for Gimbal Pose
Dynamic weighted graph of targets
DWG: To determine an optimal gimbal pointing direction for the purpose of minimizing the overall uncertainty of targetsDWG: represents the connection among targetsd: estimated distanceSigma: estimated position varianceDWG: computed in each iterationIth colum: estimated density of targets near target i14
Candidate Generation for Gimbal PoseCheck FOV Limited FOVCamera gimbal pointing direction
DWG generates candidates for MPCFinding min leads to optimal gimbal pose .ground location that matches the center of selected sensor FOV as destinationUAV trajectory: 15
Target EstimationsGimbal CandidatesMPC TechniqueTarget EstimationsDWGFOV TestGimbal CandidatesMPC TechniqueSharma & Pack method:Proposed method:
Sharma & Pack MethodR. Sharma and D. Pack, Cooperative Sensor Resource Management for Multi Target Geo-localization using Small Fixed-wing Unmanned Aerial Vehicles. in Proc. AIAA Guidance, Navigation, and Control (GNC) Conference, American Institute of Aeronautics and Astronautics, 2013.Develop a vision based cooperative sensor fusion technique to geo-locate multiple mobile ground targets usingDevelop a cooperative sensor resource manager using Model Predictive Control
Candidate Generation for Gimbal PoseModel Predictive Control(MPC)
Outline:IntroductionProblem Statement Sensor ManagementExperimental ResultsConclusion
Experimental Results
Experimental Results
Experimental Results
Experimental ResultsAVERAGE GEO-LOCATION ERRORS OF FIVE TARGETS FOR THE 100 EXPERIMENTS USING THE PROPOSED METHOD AND THE ONE REPORTED IN [9].Target No.12345North Position(m)15.4910.7324.8324.4615.26North Position(m)[9]11.7812.5426.3231.923.12East Position(m)13.586.0814.288.717.6East Position(m)[9]9.6910.2816.3813.711.41
Experimental ResultsOverall ErrorError (m)Improvement (%)Overall Error in North Position (m) Overall Error in North Position (m)[9]18.1521.1414
Overall Error in East Position (m) Overall Error east Position (m)[9]9.9411.9116
OVERALL AVERAGE OF GEO-LOCATION ERRORS
Experimental Results
Conclusion And Future WorksA new sensor management technique for UAVs tracking multiple targetsA dynamic weighted graphA Model Predictive Control technique
Future WorkMultiple UAVs cooperatively tracking multiple targets
Questions?
Thank You