handbook of multisensor data fusion phần 9 pdf

Interaction DATA FUSION DOMAIN Source Pre-Processing Level One Object Refinement Level Two Situation Refinement Level Three Threat Refinement Level Four Process Refinement Database Manag

estimation The Level 1 process results in an evolving database that contains estimates of the position, velocity, attributes, and identities of physically constrained entities (e.g., targets and emitters) Subse-quently, automated reasoning methods are applied in an attempt to perform automated situation assess-ment and threat assessassess-ment These automated reasoning methods are drawn from the discipline of artificial intelligence

Ultimately, the results of this dynamic process are displayed for a human user or analyst (via a human-computer interface (HCI) function) Note that this description of the data fusion process has been greatly simplified for conceptual purposes Actual data fusion processing is much more complicated and involves

an interleaving of the Level 1 through Level 3 (and Level 4) processes Nevertheless, this basic orientation

is often used in developing data fusion systems: the sensors are viewed as the information source and the human is viewed as the information user or sink In one sense, the rich information from the sensors (e.g., the radio frequency time series and imagery) is compressed for display on a small, two-dimensional computer screen

Bram Ferran, the vice president of research and development at Disney Imagineering Company, recently pointed out to a government agency that this approach is a problem for the intelligence community Ferran8 argues that the broadband sensor data are funneled through a very narrow channel (i.e., the computer screen on a typical workstation) to be processed by a broadband human analyst In his view, the HCI becomes a bottleneck or very narrow filter that prohibits the analyst from using his extensive pattern recognition and analytical capability Ferran suggests that the computer bottleneck effectively defeats one million years of evolution that have made humans excellent data gatherers and processors Interestingly, Clifford Stoll9,10 makes a similar argument about personal computers and the multimedia misnomer Researchers in the data fusion community have not ignored this problem Waltz and Llinas3 noted that the overall effectiveness of a data fusion system (from sensing to decisions) is affected by the efficacy of the HCI Llinas and his colleagues11 investigated the effects of human trust in aided adversarial decision support systems, and Hall and Llinas12 identified the HCI area as a key research need for data fusion Indeed, in the past decade, numerous efforts have been made to design visual environments, special displays, HCI toolkits, and multimedia concepts to improve the information display and analysis process Examples can be found in the papers by Neal and Shapiro,13 Morgan and Nauda,14 Nelson,15 Marchak and Whitney,16 Pagel,17 Clifton,18 Hall and Wise,19 Kerr et al.,20 Brendle,21 and Steele, Marzen, and Corona.22

A particularly interesting antisubmarine warfare (ASW) experiment was reported by Wohl et al.23 Wohl and his colleagues developed some simple tools to assist ASW analysts in interpreting sensor data The tools were designed to overcome known limitations in human decision making and perception Although very basic, the support tools provided a significant increase in the effectiveness of the ASW analysis The experiment suggested that cognitive-based tools might provide the basis for significant improvements in

FIGURE 19.1 Joint directors of laboratories (JDL) data fusion process model.

Interaction

DATA FUSION DOMAIN

Source Pre-Processing

Level One Object Refinement

Level Two Situation Refinement

Level Three Threat Refinement

Level Four Process Refinement

Database Management System

Support Database

Fusion Database

20 Assessing the Performance of Multisensor Fusion Processes

20.1 Introduction

20.2 Test and Evaluation of the Data Fusion Process

Establishing the Context for Evaluation • T&E Philosophies • T&E Criteria • Approach to T&E • The T&E Process — A Summary

20.3 Tools for Evaluation: Testbeds, Simulations, and Standard Data Sets

20.4 Relating Fusion Performance to Military Effectiveness — Measures of Merit

20.5 Summary

References

20.1 Introduction

In recent years, numerous prototypical systems have been developed for multisensor data fusion A paper

by Hall, Linn, and Llinas1 describes over 50 such systems developed for DoD applications even some 10 years ago Such systems have become ever more sophisticated Indeed, many of the prototypical systems summarized by Hall, Linn, and Llinas1 utilize advanced identification techniques such as knowledge-based or expert systems, Dempster-Shafer interface techniques, adaptive neural networks, and sophisti-cated tracking algorithms

While much research is being performed to develop and apply new algorithms and techniques, much less work has been performed to formalize the techniques for determining how well such methods work

or to compare alternative methods against a common problem The issues of system performance and system effectiveness are keys to establishing, first, how well an algorithm, technique, or collection of techniques performs in a technical sense and, second, the extent to which these techniques, as part of a system, contribute to the probability of success when that system is employed on an operational mission

An important point to remember in considering the evaluation of data fusion processes is that those processes are either a component of a system (if they were designed-in at the beginning) or they are enhancements to a system (if they have been incorporated with the intention of performance enhance-ment) Said otherwise, it is not usual that the data fusion processes are “the” system under test; data fusion processes are said to be designed into systems rather than being systems in their own right What

is important to understand in this sense is that the data fusion processes contribute a marginal or piecewise James Llinas

State University of New York

21 Dirty Secrets

in Multisensor Data Fusion*

21.1 Introduction

21.2 The JDL Data Fusion Process Model

21.3 Current Practices and Limitations in Data Fusion

Level 1: Object Refinement • Level 2: Situation Refinement •

Level 3: Threat Refinement • Level 4: Process Refinement •

Human-Computer Interface (HCI) • Database Management

21.4 Research Needs

Data Sources • Source Preprocessing • Level 1: Object Refinement • Level 2: Situation Refinement and Level 3:

Threat Refinement • Human-Computer Interface (HCI) •

Database Management • Level 4: Processing • Infrastructure Needs

21.5 Pitfalls in Data Fusion

21.6 Summary

References

21.1 Introduction

Over the past two decades, an enormous amount of Department of Defense (DoD) funding has been applied to the problem of data fusion systems, and a large number of prototype systems have been implemented.1 The data fusion community has developed a data fusion process model,2 a data fusion lexicon,3 and engineering guidelines for system development.4 Although a significant amount of progress has been made,5,6 much work remains to be done Hall and Garga,7 for example,identified a number of pitfalls or problem areas in implementing data fusion systems Hall and Llinas8 described some short-comings in the use of data fusion systems to support individual soldiers, and M J Hall, S A Hall, and Tate9 addressed issues related to the effectiveness of human-computer interfaces for data fusion systems This chapter summarizes recent progress in multisensor data fusion research and identifies areas in which additional research is needed In addition, it describes some issues — or dirty secrets — in the current practice of data fusion systems

*This chapter is based on a paper by David L Hall and Alan N Steinberg, Dirty secrets of multisensor data fusion,

Proceedings of the 2000 MSS National Symposium on Sensor Data Fusion, Vol 1, pp 1–16, June 2000, San Antonio, TX.

David L Hall

The Pennsylvania State University

Alan N Steinberg

Utah State University

IV Sample

Applications

22 A Survey of Multisensor Data Fusion Systems Mary L Nichols

Introduction • Recent Survey of Data Fusion Activities • Assessment of System Capabilities

23 Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems Carl S Byington and Amulya K Garga

Introduction • Aspects of a CBM System • The Diagnosis Problem • Multisensor Fusion Toolkit • Application Examples • Concluding Remarks

24 Information Technology for NASA in the 21st Century Robert J Hansen, Daniel Cooke, Kenneth Ford and Steven Zornetzer

Introduction • NASA Applications • Critical Research Investment Areas for NASA • High-Performance Computing and Networking • Conclusions

25 Data Fusion for a Distributed Ground-Based Sensing System Richard R Brooks

Introduction • Problem Domain • Existing Systems • Prototype Sensors for SenseIT • Software Architecture • Declarative Language Front-End • Subscriptions •

Mobile Code • Diffusion Network Routing • Collaborative Signal Processing •

Information Security • Summary

26 An Evaluation Methodology for Fusion Processes Based on Information Needs

Hans Keithley

Introduction • Information Needs • Key Concept • Evaluation Methodology

A Survey of Multisensor Data Fusion Systems

22.1 Introduction

22.2 Recent Survey of Data Fusion Activities

22.3 Assessment of System Capabilities

References

22.1 Introduction

During the past two decades, extensive research and development on multisensor data fusion has been performed for the Department of Defense (DoD) By the early 1990s, an extensive set of fusion systems had been reported for a variety of applications ranging from automated target recognition (ATR) and identification-friend-foe-neutral (IFFN) systems to systems for battlefield surveillance Hall, Linn, and Llinas1 provided a description of 54 such systems and an analysis of the types of fusion processing, the applications, the algorithms, and the level of maturity of the reported systems Subsequent to that survey, Llinas and Antony2 described 13 data fusion systems that performed automated reasoning (e.g., for situation assessment) using the blackboard reasoning architecture By the mid-1990s, extensive commer-cial off-the-shelf (COTS) software was becoming available for different data fusion techniques and for decision support Hall and Linn3 described a survey of COTS software for data fusion and Buede4,5 performed surveys and analyses of COTS software for decision support

This chapter presents a new survey of data fusion systems for DoD applications The survey was part

of an extensive effort to identify and assess DoD fusion systems and activities This chapter summarizes

79 systems and provides an assessment of the types of fusion processing performed and their operational status

22.2 Recent Survey of Data Fusion Activities

A survey of DoD operational, prototype, and planned data fusion activities was performed in 1999–2000 The data fusion activities that were surveyed had disparate missions and provided a broad range of fusion capabilities They represented all military services The survey emphasized the level of fusion provided (according to the JDL model described in Chapter 2 of this book) and the capability to fuse different types of intelligence data A summary of the survey results is provided here

In the survey, a data fusion system was considered to be more than a mathematical algorithm used to automatically achieve the levels of data fusion described in Chapter 2 In military applications, data fusion

is frequently accomplished by a combination of the mathematical algorithms (or “fusion engines”) and Mary L Nichols

The Aerospace Corporation

23 Data Fusion for Developing Predictive

Diagnostics for Electromechanical

Systems

23.1 Introduction

Condition-Based Maintenance Motivation

23.2 Aspects of a CBM System

23.3 The Diagnosis Problem

Feature-Level Fusion • Decision-Level Fusion • Model-Based Development

23.4 Multisensor Fusion Toolkit

23.5 Application Examples

Mechanical Power Transmission • Fluid Systems •

Electrochemical Systems

23.6 Concluding Remarks

Acknowledgments

References

23.1 Introduction

Condition-based maintenance (CBM) is a philosophy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure By contrast, time-based or use-based maintenance involves performing periodic maintenance after specified periods of time or hours of operation CBM has the potential to decrease life-cycle maintenance costs (by reducing unnecessary maintenance actions), increase operational readiness, and improve safety

Implementation of condition-based maintenance involves predictive diagnostics (i.e., diagnosing the current state or health of a machine and predicting time to failure based on an assumed model of anticipated use) CBM and predictive diagnostics depend on multisensor data — such as vibration, temperature, pressure, and presence of oil debris — which must be effectively fused to determine machinery health Indeed, Hansen et al suggested that predictive diagnostics involves many of the same functions and challenges demonstrated in more traditional Department of Defense (DoD) applications

of data fusion (e.g., signal processing, pattern recognition, estimation, and automated reasoning).1 This chapter demonstrates the potential for technology transfer from the study of CBM to DoD fusion applications

Carl S Byington

The Pennsylvania State University

Amulya K Garga

The Pennsylvania State University