Show simple item record

dc.contributor.authorTobias, Martinen_US
dc.date.accessioned2006-06-09T18:19:00Z
dc.date.available2006-06-09T18:19:00Z
dc.date.issued2006-04-07en_US
dc.identifier.urihttp://hdl.handle.net/1853/10514
dc.description.abstractThe probability hypothesis density (PHD), popularized by Ronald Mahler, presents a novel and theoretically-rigorous approach to multitarget, multisensor tracking. Based on random set theory, the PHD is the first moment of a point process of a random track set, and it can be propagated by Bayesian prediction and observation equations to form a multitarget, multisensor tracking filter. The advantage of the PHD filter lies in its ability to estimate automatically the expected number of targets present, to fuse easily different kinds of data observations, and to locate targets without performing any explicit report-to-track association. We apply a particle-filter implementation of the PHD filter to realistic multitarget, multisensor tracking using passive coherent location (PCL) systems that exploit illuminators of opportunity such as FM radio stations. The objective of this dissertation is to enhance the usefulness of the PHD particle filter for multitarget, multisensor tracking, in general, and within the context of PCL, in particular. This involves a number of thrusts, including: 1) devising intelligent proposal densities for particle placement, 2) devising a peak-extraction algorithm for extracting information from the PHD, 3) incorporating realistic probabilities of detection and signal-to-noise ratios (including multipath effects) to model realistic PCL scenarios, 4) using range, Doppler, and direction of arrival (DOA) observations to test the target detection and data fusion capabilities of the PHD filter, and 5) clarifying the concepts behind FISST and the PHD to make them more accessible to the practicing engineer. A goal of this dissertation is to serve as a tutorial for anyone interested in becoming familiar with the probability hypothesis density and associated PHD particle filter. It is hoped that, after reading this thesis, the reader will have gained a clearer understanding of the PHD and the functionality and effectiveness of the PHD particle filter.en_US
dc.format.extent19093018 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectParticle filtersen_US
dc.subjectFinite set statistics
dc.subject.lcshTracking radar Mathematicsen_US
dc.subject.lcshRandom setsen_US
dc.subject.lcshMultisensor data fusionen_US
dc.titleProbability Hypothesis Densities for Multitarget, Multisensor Tracking with Application to Passive Radaren_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.advisorCommittee Chair: Lanterman, Aaron; Committee Member: McClellan, James; Committee Member: Vidakovic, Brani; Committee Member: Williams, Douglas; Committee Member: Yezzi, Anthonyen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record