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

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.

Grantor dc:publisher

Temple University. Libraries

Year dc:date.issued

2019

Author dc:creator

• Brodovsky, James A.

Advisor dc:contributor.advisor

• Dames, Philip

Committee members dc:contributor.committeemember

• Jacobs, Daniel A.
• Peridier, Vallorie J.

• Robotics
• Remote sensing
• MHT
• PhD filter
• Target tracking

Statement dc:rights

• IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/20.500.12613/860

OAI identifier oai:identifier

oai:scholarshare.temple.edu:20.500.12613/860