COURSE # ROO-407
INTRODUCTION TO SENSORS and MULTI-TARGET, MULTI-SENSOR DATA FUSION
TECHNIQUES FOR DETECTION, IDENTIFICATION, AND TRACKING
July 19-22, 1999 in Washington, DC
An informative presentation that addresses the complexities of this multi-disciplinary topic and provides a clear picture of the technology.
This course presents the theory of operation of active and passive
millimeter-wave and infrared sensors in a practical and easy to
understand manner; it also describes the methods by which data is
combined from diverse sensors to improve the probability of correct
target detection, classification, identification, and tracking. The
course offers a system-level discussion of sensors' characteristics and
data fusion technologies as they relate to command, control,
communication and intelligence (C3I).
Technology aspects of data fusion including detection (or decision)
theory, estimation theory, digital signal processing, and parametric and
non-parametric data fusion techniques (including fuzzy logic and neural
networks) are covered. The course addresses data fusion related areas
of C3I including sensors systems, sensor management, data fusion for
state estimation, data fusion for target identification, and systems
performance evaluation.
Practical examples help demonstrate the advantages of multi-sensor data
fusion in systems that use laser radar (imagery and range data), forward
looking IR sensor (imagery data), IR search and track systems (angular
position data), electronic support measures (ESM) (kinematic and
attribute data), and other intelligence data.
Applications and benefits:
You will benefit by enhancing your understanding of the :
- Reasons for, and the techniques employed in, multisensor data fusion for decision process enhancement.
- Multisensor data fusion principles, algorithms and techniques.
- Practical applications.
Who should attend:
Engineers, scientists, managers, designers, and users of multi-sensor
data fusion for target detection, classification, identification, and
tracking. Those interested in selecting appropriate sensors for
specific applications and applying data fusion techniques to advanced
dynamic systems, such as classification of airborne targets,
ground-based targets, and underwater targets. Developers and users of
real-time algorithms for intelligent machine development and multiple
sensor technologies for non-cooperative target recognition. This course
has no prerequisites; however, a general background in electrical
engineering, mathematics, or statistics is beneficial, but not required,
for an understanding of the concepts presented in the course.
Course Outline:
- Introduction and Overview
- Scope of the course
- Defense Applications of Multi-sensor Suites and Data Fusion
- Antisubmarine warfare
- Tactical air warfare
- Ground battlefield warfare
- Military data fusion architecture
- Sensor Systems
- Benefits of multiple sensor systems
- Data fusion definition and architectures
- Resolution, hardware state-of-the-art, and strengths and weaknesses of
infrared and millimeter-wave sensors
- Atmospheric and obscurant effects
- Millimeter-wave radar (active) sensors
- Radar configurations
- Noise figure
- Pulse radar
- FMCW radar
- CW Doppler radar
- Pulse Doppler radar and range and velocity ambiguities
- Synthetic aperture radar
- Optimizing target detection in ground clutter
- High-background clutter signature data
- Detection and classification signal processing techniques
- Processing of intermediate frequency data in an FMCW radar
- Radar range equation
- Diversity and integration gain
- Temporal and frequency decorrelation
- Scene modeling with fractals
- Passive millimeter-wave sensors
- Planck radiation law
- Radiative transfer theory
- Total power, Dicke, and noise injection radiometers
- Effect of signal-to-noise and signal-to-clutter ratios on
detection probability
- High-background clutter signature data
- Passive infrared sensors
- Sensor design issues
- Scene-imaging techniques
- Object discrimination levels
- Performance measures for imaging (FLIR) and non-imaging (IRST) sensors
- Target classification models
- Sampling frequency selection
- Detection theory for passive and active infrared sensors
- General application of signal processing to multi-pixel imagery
- Laser radar
- Military applications
- Solid state radars
- Characteristics of laser radar imagery
- Laser radar range equation
- Noise sources and signal-to-noise ratio
- Target fluctuation characteristics
- Detection probability calculation for laser radars
- Summary of millimeter-wave and infrared signal detection theory
- Windows and domes for dual-wavelength sensors
- General requirements
- Dielectric theory
- Physical properties of dual-wavelength window and dome materials
- Data Fusion Algorithms
- Level 1, 2, 3, and 4 processing
- Taxonomy of detection, classification, and identification data fusion
algorithms
- Physical models
- Feature-based inference
- Parametric (Bayesian, Dempster-Shafer, others)
- Non-parametric (templates, neural networks)
- Neural networks
- Fundamental concepts and models of neural networks
- Multi-layer perceptron classifiers
- Multi-layer feed-forward networks (Kohonen network)
- Grossberg adaptive resonance network
- Hopfield network
- Cognitive-based (knowledge-based, fuzzy theory, others)
- Taxonomy of state estimation and tracking data fusion algorithms
- Search direction
- Association and correlation of data and tracks
- Data alignment
- Data and object correlation
- Position, kinematic, and attribute estimation
- Sensor Management as an Example of Level 4 Fusion Processing
- Sensor management functions
- Establishing target priority
- Sensor-to-target assignment
- Sensor management applications
- Target Tracking in a Cluttered Environment
- Validation of sensor measurements
- Single target in clutter
- Multiple targets in clutter
- Single Target Tracking in a Cluttered Environment
- Nearest-neighbor standard filter
- Track-splitting filter
- Probabilistic data association filter (PDAF)
- Maneuvering Target Tracking
- Multiple-model approach
- Maneuver hypotheses
- White noise and colored noise modeling of maneuvers
- Variable dimension filtering approach
- Maneuvering target in clutter
- Multiple Targets in a Cluttered Environment
- A joint likelihood method for track formation
- Joint probabilistic data association filter (JPDAF)
- A multiple hypothesis filter
- Multisensor Multi-target Tracking
- Report-to-track fusion for target tracking
- Track-to-track fusion for target tracking
Text: Sensor and Data Fusion Concepts and Applications, 2nd Edition Lawrence A. Klein, 1997.
About the Instructors
Lawrence A. Klein is a consultant specializing in developing multiple
sensors concepts for tactical military and reconnaissance applications.
While at Hughes Aircraft, he developed missile deployment strategies and
sensors used in missile guidance. As a systems manager at Aerojet
ElectroSystems, he was responsible for the conceptual design and
execution of programs that integrated active and passive millimeter-wave
and infrared multispectral sensors in satellites and smart
"fire-and-forget" weapons. He was the program manager of three
Manufacturing Methods and Techniques projects that lowered the cost of
millimeter-wave integrated circuits. At Honeywell, he developed passive
millimeter-wave midcourse missile guidance systems and millimeter-wave
sensors for mine applications. Dr. Klein's first book, Sensor and Data
Fusion Concepts and Applications, was published by the SPIE in 1993.
His second book, Millimeter- Wave and Infrared Multisensor Design and
Signal Processing published by Artech House in August 1997, discusses
multisensor applications, design, and performance.
Dr. Klein received his Ph.D. in Electrical Engineering from New York
University in 1973 and is a past reviewer for the IEEE Transactions on
Antennas and Propagation, and Geoscience and Remote Sensing.
Martin Dana is a Senior Scientist at Raytheon Systems Company (formerly
Hughes Aircraft Company) in El Segundo, California. He has more than 25
years of experience in the analysis and design of multisensor tracking
and identification systems for air defense and air traffic control. His
specific areas of interest include track acquisition in cluttered
environments and multiple sensor registration and alignment. He has
installed and verified the operation of multiple radar air defense
systems in Europe, the Middle East, Japan, and Korea.
Dr. Dana received a Ph.D. in mathematics from Washington State
University in 1972. He has published papers on data fusion in various
NATO AGARD publications and the proceedings of the US Combat ID and Data
Fusion Symposia. His early work in registration for multiple sensor
tracking may be found in Chapter 5 of Bar-Shalom's book,
Multitarget-Multisensor Tracking published by Artech House in 1990. Dr.
Dana is a reviewer for the IEEE Transactions on Aerospace and Electronic
Systems.
Details:
Course: ROO-407 Duration: 4 Days FEE: $1,395 CEUs: 2.88
Please direct any additional inquiries regarding our courses to Robert Blakely, Program Director, by e-mail, FAX: (301) 871-4942 or TELEPHONE: (301) 871-9608.
Call toll free 1-800-683-7267 from anywhere in the Continental U.S. or CANADA.
Last modified July 5, 1999.
