Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

Abstract

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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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2017
Accession Number
AD1046858

Entities

People

  • Benjamin Kalkwarf

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Applied Mathematics
  • Bayesian Networks
  • Coast Guard
  • Detection
  • Mathematics
  • Naval Operations
  • Navy
  • Operations Research
  • Probability
  • Probability Distributions
  • Search Theory
  • Submarine Warfare
  • Underwater Vehicles
  • Unmanned Aerial Vehicles
  • Unmanned Underwater Vehicles

Fields of Study

  • Computer science

Readers

  • Phased Array Antenna Design.
  • Sensor Fusion and Tracking Systems.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms