Search parameter optimization for discrete, Bayesian, and continuous search algorithms
17Sep_Kalkwarf_Benjamin.pdf (5.362Mb)Date
2017-09Metadata
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Search and Detection Theory is the overarching field of study that covers many scenarios. These range from simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and Bayesian algorithm parameters to analyze the optimization. It also expands on previous research of two searchers with search radii coupled to their speed, executing three search patterns: inline spiral search, inline ladder search, and a multipath ladder search. Analysis reveals that the Bayesian search and discrete search work similarly, but the Bayesian search algorithm provides a more useful output in location probability. Results from the continuous search were similar to previous research, but variance in time to detection became more complex than basic increasing or decreasing ranges.
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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.Collections
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