Particle filtering methods for incorporating intelligence updates
dc.contributor.advisor | Singham, Dashi I. | |
dc.contributor.author | Nunez, Jesse A. | |
dc.date | Mar-17 | |
dc.date.accessioned | 2017-05-10T16:31:57Z | |
dc.date.available | 2017-05-10T16:31:57Z | |
dc.date.issued | 2017-03 | |
dc.identifier.uri | http://hdl.handle.net/10945/53026 | |
dc.description.abstract | Due to uncertainty in target locations, stochastic models are implemented to provide a representation of location distribution. The reliability of these models has a profound effect on the ability to successfully interdict these targets. A key factor in the reliability of a model is the incorporation of information updates. A common method for incorporating information updates is Kalman filtering. However, given the probable nonlinear and non-Gaussian nature of target movement models, the fidelity of solutions provided by Kalman filtering could be significantly degraded. A more robust methodology needs to be employed. This thesis uses an updating algorithm known as particle filtering to incorporate information updates concerning the target's position. Particle filtering is a nonparametric filtering technique that is adaptable and flexible. The particle filter is incorporated into a model that uses a stochastic process known as a Brownian bridge to model target movement. A Brownian bridge models target movement with minimal information and allows for uncertainty during periods when target location is unknown. As new intelligence arrives, the particle filter is used to update a probabilistic heat map of target position. The main goal of this thesis is to design a stochastic model integrating both the Brownian bridge model and particle filtering. | en_US |
dc.description.uri | http://archive.org/details/particlefilterin1094553026 | |
dc.publisher | Monterey, California: Naval Postgraduate School | en_US |
dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | en_US |
dc.title | Particle filtering methods for incorporating intelligence updates | en_US |
dc.type | Thesis | en_US |
dc.contributor.secondreader | Atkinson, Michael P. | |
dc.contributor.department | Operations Research (OR) | |
dc.subject.author | Brownian bridge movement models | en_US |
dc.subject.author | particle filter | en_US |
dc.subject.author | stochastic modeling | en_US |
dc.subject.author | nonlinear filtering | en_US |
dc.subject.author | simulations | en_US |
dc.description.recognition | Outstanding Thesis | |
dc.description.service | Lieutenant, United States Navy | en_US |
etd.thesisdegree.name | Master of Science in Operations Research | en_US |
etd.thesisdegree.level | Masters | en_US |
etd.thesisdegree.discipline | Operations Research | en_US |
etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
dc.description.distributionstatement | Approved for public release; distribution is unlimited. |
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