A Brownian bridge movement model to track mobile targets
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Authors
Cheng, Chun Chieh
Subjects
Brownian bridge movement models
unmanned sensors
probability of detection
search and detection
simulations
unmanned sensors
probability of detection
search and detection
simulations
Advisors
Singham, Dashi I.
Date of Issue
2016-09
Date
Sep-16
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
The Brownian bridge movement model (BBMM) models target movement between two known points as a Brownian bridge. This thesis extended the BBMM to account for multiple starting and ending points and to account for intelligence inputs midway through the target movement. The BBMM is applied to a military scenario where U.S. forces are conducting surveillance to monitor the breakout of Chinese forces in the South China Sea. Probability heat maps, depicting the probability of a target location at discrete times, are generated through simulations in MATLAB. Using the heat maps, this thesis developed an algorithm to automate the placement of sensors to detect the target. This thesis focused on the use of a network of unmanned sensors as the means for target detection. The relationship between the sensors' attributes and the probability of detection is explored through a meta-experiment. The experiment utilizes a three-stage algorithm that generates heat maps, deploys sensors and randomizes intelligence inputs, and measures the probability of detection. A trade-off analysis was conducted and showed that to achieve a higher probability of detection, it is more effective to have sensors cover a wider area at fewer discrete points in time than to have a greater number of discrete looks using sensors covering smaller areas.
Type
Thesis
Description
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
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Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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Copyright is reserved by the copyright owner.