1. Lesson 2: The JDL Model
David L. Hall

2. Lesson Objectives
Introduce the Joint Directors of Laboratories (JDL) Data Fusion Process Model
Compare the JDL Model with competing models

3. Introduction and Motivation
The JDL Data Fusion Process Model has proliferated throughout the DoD fusion community
Original creation in 1991/1992
Multiple extensions/modifications
Proliferation in books, papers, conferences
Utilized in organizations, RFPs, etc.
Emerging models from other community models and applications
Decision support
Cognitive sciences

4. Overview of Models Surveyed

5. Origin of the JDL Data Fusion Model
JDL Data Fusion Sub-Panel (and working group)
Meeting in State College, PA
Development of briefing for the Office of Naval Intelligence
Members of the Joint Directors of Laboratories (JDL) Data Fusion Working Group: Ed Waltz, Chee Chong, Frank White, Otto Kessler, David Hall, James Llinas and Alan Steinberg

6. JDL Data Fusion Model
Top level view of the JDL data fusion process model (Hall and McMullen (2004))

7. Example of 2nd Level in JDL Model Hierarchy

8. Example of 3rd Layer in JDL Model Hierarchy

9. Transformation of Requirements for the Information Process (TRIP) Model
The TRIP waterfall model for data fusion system development (adapted from Kessler and Fabien (2001))

10. Omnibus Model
Signal Processing
Sensing
Observe
Pattern Processing
Feature Extraction
Orient
Decision Making
Context Processing
Decide
Control
Resource Tasking
Act
Sensor Management
Hard Decision Fusion
Soft Decision Fusion
Sensor Data Fusion
Feature Fusion
The Omnibus Model for decision making and data fusion (adapted from Bedworth and Obrien (1999))

11. Dasarathy’s Functional Model
Table 2: Components of Dasarathy’s Model
Input
Output
Notation
Analogues
Data
DAI-DAO
Data-level fusion
Features
DAI-FEO
Feature selection and feature extraction
FEI-FEO
Feature-level fusion
Decisions
FEI-DEO
Pattern recognition and pattern processing
DEI-DEO
Decision-level fusion
Three general levels of abstraction in fusion processing:
Data level - integration of raw observations and can occur only in the case when the observations are of the same type
Feature level - assumes that each stream of sensory data is first analyzed for features, after which the features themselves are fused
Decision Level - based on the fusion of individual mode decisions or interpretations

12. Recognition Primed Decision-Making
Recognition Primed Decision Making under dynamic evolving situations (adapted from Klein (1999)).

13. Intelligent Agents to Support Team Cognition
Motivation and Vision
Effective human teams use shared mental models (SMM) to anticipate & satisfy the needs of teammates.
Our vision: Empower software agents with a cognitively inspired SMM to support human decision makers to overcome information overload under time stress.
R-CAST Implementation
Inspired by Recognition-Primed Decision Model (RPD).
Integrates information seeking in a collaborative decision-making process
Supports context-centric information sharing.
Enables collaborative/automated reasoning.
A cyber-advisory team of intelligent
agents supports
collaborative decision making and
mitigate cognitive biases.

14. Boyd’s OODA Loop The original OODA loop (adapted from Boyd (1987))
Observe
Orient
Decide
Act
Cover of Boyd’s biography: Downloaded from web site on July 27, 2008
The original OODA loop (adapted from Boyd (1987))

15. Boyd’s Updated OODA Loop (1996)
Observe
Orient
Decide
Act
Observations
Cultural Traditions
Genetic Heritage
New Information
Previous Experience
Analysis & Synthesis
Decision Hypothesis
Unfolding Interaction with Environment
Implicit Guidance & Control
Feedback
Action
Boyd’s modern OODA Loop decision process model (1996)

16. Variations of OODA Loop Model
OODA Variations
Variations of OODA Loop Model
Model
Description
References
CECA
An modified version of OODA using modern theories of cognition
Bryant, 2005
M-OODA
Modular OODA adds process, state, & control components
Rousseau & Breton, 2004
T-OODA
Team OODA is a modified M-OODA scoped for team decision making
C-OODA
Cognitive OODA includes the theories of RPD and SA
Rousseau & Breton, 2005
D-OODA
Dynamic OODA adds planning and Sensemaking with a focus to increase the speed of decision-making
Brehmer, 2005
SADT OODA
A functional system design representation of OODA
Grant, 2005

17. Evaluating the Scope of Models
Sensing & Data Gathering
Data Fusion Core Functions
Human Computer Interaction
Situation Assessment
Decision Making
Action & Feedback
JDL
TRIP
Dasarathy
Omnibus
RPD
OODA
OODA Variations
Key:
Strong Coverage
Weak Coverage

18. Endsley’s Model of Situational Awareness
Downloaded on July 29, 2008 from

19. Summary JDL Fusion model has evolved to remain useful
Extensions to level’s 0 and level 5 increase the utility and coverage of the model
Future extensions
More attention to decision-making
Incorporation of cognitive models
Maturation of 2nd & 3rd level hierarchy for levels 2 and 3
Blurring of levels
Explicit understanding of distributed processing effects
Extension of the concepts of data and information

20. Lesson 2 Assignments Preview the on-line lesson 2 materials
Read Chapter 2 of Hall and McMullen
Read Hall, Hellar, Llinas & McNeese 2007 paper
Writing assignment 3: Using the example introduced in the first session, write a one page paper describing how the JDL model represents components of that process, e.g., what are the sensing and diagnosis functions that relate to levels 1, 2, 3, 4 and 5. What are the sensor inputs? What are the human inputs? What inferences or decisions are trying to be made? Why is it necessary to use multiple sensors?

21. Data Fusion Tip of the Week
Tip: “It helps to remember that there is no spoon” from the movie, The Matrix, March 1999
Data Fusion Corollary: “It helps to remember, there are no levels of data fusion”