Uncertainty-Sensitive Heterogeneous Information Fusion

Assessing Threat with Soft, Uncertain, and Conflicting Evidence

by Paul K. Davis, Walter L. Perry, John S. Hollywood, David Manheim

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An element of thwarting terrorist attacks is observing suspicious individuals over time with such diverse means as scanners and other devices, travel records, behavioral observations, and intelligence sources. Such observations provide data that are often both complex and "soft" — i.e., qualitative, subjective, fuzzy, or ambiguous — and also contradictory or even deceptive. Analysts face the challenge of heterogeneous information fusion — that is, combining such data to form a realistic assessment of threat. This report presents research on various heterogeneous information fusion methods and describes a research prototype system for fusing uncertainty-sensitive heterogeneous information. The context is counterterrorism, for both military and civilian applications, but the ideas are also applicable in intelligence and law enforcement.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Concepts, Methods, and a Research Platform

  • Chapter Three

    Creating Synthetic Data: Vignettes for Testing

  • Chapter Four

    Simple and Bayesian Fusion Methods

  • Chapter Five

    The Maximum Entropy/Minimum Penalty Fusion Method

  • Chapter Six

    Illustrative Analysis

  • Chapter Seven

    Conclusions and Recommendations

  • Appendix A

    Defining Threat

  • Appendix B

    A Factor-Tree Model for Propensity for Terrorism (PFT)

  • Appendix C

    Extending Elementary Bayes Updating

This research was sponsored by the Office of Naval Research and conducted within the International Security and Defense Policy Center of the RAND National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community.

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