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Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform

Holsinger, Seth D
http://orcid.org/0000-0002-6494-1796
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365

Abstract Details

2019, MS, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
This research focuses on solving the problem of tracking multiple ground targets from a single or multiple UAV's utilizing a dynamic clustering algorithm. Each UAV is assumed to have only one camera that is mounted on a two axis gimbal. The location of each of the targets can be ascertained by using a geolocation method with the camera as the primary sensor. The targets will not be able to transmit any of their location data to the UAV's. An extended Kalman filter is used to estimate the location and velocity of the targets with the afore mentioned geolocation used as the update step. The algorithm uses the estimated target locations and the distance from the UAV to different point locations to find the maximum number of targets that the camera can view at once. These clusters of points are then subject to a cost function where the goal is to minimize the overall uncertainty of all of the system. While also staying below a maximum allowable uncertainty for individual targets. The approach taken by this method is scalable with the number of UAV's and ground targets that it can track to allow for it to be used in a wide variety of circumstances. The targets future locations can also be evaluated by the clustering algorithm to get the locations that the camera should point in order to minimize the uncertainty.
Rajnikant Sharma, Ph.D. (Committee Chair)
Manish Kumar, Ph.D. (Committee Member)
George T. Black, M.S. (Committee Member)
82 p.
Aerospace Materials
Aerospace; Coordination; UAV; Autonomous; Intelligent Systems

Recommended Citations

Citations

  • Holsinger, S. D. (2019). Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365

    APA Style (7th edition)

  • Holsinger, Seth. Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform. 2019. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365.

    MLA Style (8th edition)

  • Holsinger, Seth. "Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform." Master's thesis, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365

    Chicago Manual of Style (17th edition)

ucin1552380066855365
396
© 2019, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.