Particle filtering methods for incorporating intelligence updates
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Authors
Nunez, Jesse A.
Subjects
Brownian bridge movement models
particle filter
stochastic modeling
nonlinear filtering
simulations
particle filter
stochastic modeling
nonlinear filtering
simulations
Advisors
Singham, Dashi I.
Date of Issue
2017-03
Date
Mar-17
Publisher
Monterey, California: Naval Postgraduate School
Language
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.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
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NPS Report Number
Sponsors
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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.