Adaptive Multi-modality Sensor Scheduling for Detection and Tracking of Smart Targets

Abstract

This paper considers the problem of sensor scheduling for the purposes of detection and tracking of "smart" targets. Smart targets are targets that are able to detect when they are under surveillance and react in a manner that makes future surveillance more difficult. We take a reinforcement learning approach to adaptively schedule a multi-modality sensor so as to most quickly and effectively detect the presence of smart targets and track them as they travel through a surveillance region. An optimal scheduling strategy, which would simultaneously address the issue of target detection and tracking, is very challenging computationally. To avoid this difficulty, we advocate a two stage approach where targets are first detected and then handed off to the tracking algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2004
Accession Number
ADA509780

Entities

People

  • Alfred O. Hero III
  • Chris Kreucher
  • Doron Blatt
  • Keith Kastella

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Science
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Electrical Engineering
  • Engineering
  • Passive Sensors
  • Probability
  • Radar
  • Reinforcement Learning
  • Scheduling (Production)
  • Simulations
  • Synthetic Aperture Radar
  • Target Detection
  • Target Tracking

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Robotics and Automation.

Technology Areas

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