Sensor Data Integrity and Mitigation of Perceptual Failures

Abstract

This report describes basic research to analyze and investigate methods for automatic detection and mitigation of sensor and perception data. Specifically, the PI will investigate 1) Metrics for sensor performance in context using multi-modal redundancy and information theoretic approaches; 2) Detection, localization and mitigation of faults and failures using hierarchical causal reasoning for diagnosis; 3) Develop methods for using low-level (e.g., sensor data fusion filter) versus high-level (autonomous inference) processes for automated selection of data to use for perception.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2012
Accession Number
ADA566335

Entities

People

  • Thierry Peynot

Organizations

  • University of Sydney

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Autonomous Systems
  • Collision Avoidance
  • Computational Science
  • Detection
  • Detectors
  • Hidden Markov Models
  • Kernel Functions
  • Motion Planning
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Simultaneous Localization And Mapping
  • Three Dimensional
  • Unmanned Ground Vehicles

Fields of Study

  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML