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    A COMPARISON OF THE PROBABILITY HYPOTHESIS DENSITY FILTER AND THE MULTIPLE HYPOTHESIS TRACKER FOR TRACKING TARGETS OF MULTIPLE TYPES

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    Genre
    Thesis/Dissertation
    Date
    2019
    Author
    Brodovsky, James A.
    Advisor
    Dames, Philip
    Committee member
    Jacobs, Daniel A.
    Peridier, Vallorie J.
    Department
    Mechanical Engineering
    Subject
    Robotics
    Remote Sensing
    Mht
    Phd Filter
    Robotics
    Target Tracking
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/860
    
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    DOI
    http://dx.doi.org/10.34944/dspace/842
    Abstract
    Robotic technology is advancing out of the laboratory and into the everyday world. This world is less ordered than the laboratory and requires an increased ability to identify, target, and track objects of importance. The Bayes filter is the ideal algorithm for tracking a single target and there exists a significant body of work detailing tractable approximations of it with the notable examples of the Kalman and Extended Kalman filter. Multiple target tracking also relies on a similar principle and the Kalman and Extended Kalman filter have multi-target implementations as well. Other method include the PHD filter and Multiple Hypothesis tracker. One issue is that these methods were formulated to only track one classification of target. With the increased need for robust perception, there exists a need to develop a target tracking algorithm that is capable of identifying and tracking targets of multiple classifications. This thesis examines two of these methods: the Probability Hypothesis Density (PHD) filter and the Multiple Hypothesis Tracker (MHT). A Matlab-based simulation of an office floor plan is developed and a simulation UGV equipped with a camera is set the task of navigating the floor plan and identifying targets. Results of these experiments indicated that both methods are mathematically capable of achieving this. However, there was a significant reliance on post-processing to verify the performance of each algorithm and filter out noisy sensor inputs indicating that specific multi-target multi-class implementations of each algorithm should be implemented with a detailed and more accurate sensor model.
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