Optimal cooperative sensing using multiple sensor platforms

dc.contributor.advisorQian, Chunjiang
dc.contributor.advisorPack, Daniel
dc.contributor.authorFarmani, Negar
dc.contributor.committeeMemberAkopian, David
dc.contributor.committeeMemberCao, Yongcan
dc.date.accessioned2024-02-09T21:11:49Z
dc.date.available2024-02-09T21:11:49Z
dc.date.issued2016
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractIn this work, we present a distributed multi-target tracking system for cooperative unmanned systems equipped with vision sensors and communication modules. The sensors have some limitations and constraints which make the target tracking challenging. We, first, address the sensor manager and the path planner problems for a single unmanned system to track multiple mobile targets in an optimal manner. To that end, a Dynamic Weighted Graph technique determines the high target density areas and a Model Predictive Control is used to optimize the gimbaled camera pose. A real-time path planner with two-time horizon steps optimizes the unmanned system path by incorporating the uncertainty of target state estimations and the relative distance among the unmanned system and the targets. Then, we incorporate a distributed task allocation algorithm to track multiple mobile targets cooperatively when the number of unmanned systems is less than the number of targets. The algorithm benefits from sharing data amongst unmanned systems. To make the system scalable, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used. The DBSCAN algorithm is integrated with the optimal sensor manager and path planner in order to distributively track a large number of targets with a limited number of resources. A set of Extended Kalman Filter (EKF) is used by each unmanned system to estimate the location of mobile targets. Later, we enhance the optimal path planner technique for cooperative unmanned systems while the communication range is limited. The new path planner technique is affected by the trade-off between communicating with neighbors and performing the assigned tasks. Finally, we address the problem of unknown delayed measurements in the observations and present a technique to improve the target tracking system by integrating the Hidden Markov Model and Out Of Order Sigma Point Kalman Filter (O3SPKF). The effective performance of the. proposed systems are shown through simulation results.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent103 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781369440225
dc.identifier.urihttps://hdl.handle.net/20.500.12588/3562
dc.languageen
dc.subjectCooperative System
dc.subjectGeo-localization
dc.subjectPath Planning
dc.subjectSensor Management
dc.subjectTarget Tracking
dc.subjectUnmanned Systems
dc.subject.classificationElectrical engineering
dc.titleOptimal cooperative sensing using multiple sensor platforms
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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