Integrated Human Behavior Modeling and Stochastic Control (IHBMSC)

Abstract

This project developed an operator error model for improving the accuracy of the operator task of discriminating objects in multiple UAV videos on the Vigilant Spirit Control Station (VSCS) ground station. This error model is idiographic in the sense that it is tailored for a specific individual. The operator error model is used in a stochastic controller which decides, based on the model uncertainty and the operator input of target/non-target, whether to revisit the object to obtain additional information or to continue to the next object. The stochastic controller computes the probability of a target given the available evidence and the reliability of the evidence as per the operator error model. Over 120 operator-in-the-loop tests were conducted to obtain sufficient data to be able to estimate the false alarm (FA) and missed detection (MD) rates over four variables (altitude, aspect angle, dwell time, workload). For a 50% target density scenario where the operator discrimination task is fairly difficult, the integrated human behavior model and stochastic controller (IHBMSC) was shown to reduce the FA rate from 35% to 5%. Also, the IHBMSC performance was found to be quite robust for lower target densities (5%) and different operators.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2014
Accession Number
ADA609457

Entities

People

  • Clayton D. Rothwell
  • Krishnamoorthy Kalyanam
  • Meir Pachter
  • Michael Patzek
  • Phillip R. Chandler
  • Sara Naderer

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Human Systems
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Aircrafts
  • Altitude
  • Computational Science
  • Control Systems
  • Databases
  • Detection
  • Detectors
  • Information Science
  • Probability
  • Random Variables
  • Stochastic Control
  • Stochastic Processes
  • Supervised Machine Learning
  • Target Recognition
  • Unmanned Aerial Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Robotics and Automation.