COURSE # TRO-397

MULTISENSOR DATA FUSION FOR MULTITARGET CLASSIFICATION AND STATE ESTIMATION


This course describes sensor and data fusion methods that improve the probability of correct target detection, classification, identification, and state estimation. These techniques combine information from collocated or dispersed sensors that utilize either similar or different technologies to generate target measurements, signatures, or imagery. The effects of the atmosphere and countermeasures on millimeter-wave and infrared sensors are presented to illustrate how the use of different phenomenology-based sensors enhances a data fusion system.

After introducing the JDL data fusion model, several methods for describing sensor and data fusion architectures are presented. Data fusion algorithm taxonomies and a general description of the algorithms and methods used for detection, classification, identification, and state estimation and tracking are discussed next. This is followed by consideration of situation and threat assessment. Subsequent sections of this course more fully develop the classical inference, Bayesian, Dempster-Shafer, voting logic, artificial neural network, and fuzzy logic data fusion algorithms. Radar tracking system design considerations, multiple sensor registration, track initiation in clutter, Kalman filtering, interacting multiple models, and data fusion maturity as it affects real-time tracking complete the in-depth topics treated in the course.

Examples are offered to demonstrate the advantages of multisensor data fusion in systems that use microwave and millimeter-wave detection and tracking radars, laser radars (imagery and range data), and forward-looking IR sensors (imagery data). Many of the data fusion techniques also apply when it is desired to combine information from almost any grouping of sensors as long as they can supply the input data required by the fusion algorithm.

Applications and benefits:

Participants benefit by enhancing their understanding of the:

The course is intended for:

Recommended Prerequisite:

There are no specific course prerequisites; however, a general background in electrical engineering, electro-optics, mathematics, or statistics is recommended for a better understanding of the concepts presented in the course.

Course Outline:


Text:

The text, Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second Edition, Lawrence A. Klein (SPIE, PM 222, 2012), and lecture notes are distributed on the first day of the course. The notes are for participants only and are not available for sale.



About the Instructor


Dr. Lawrence A. Klein, PhD, consults in developing multiple sensor concepts for tactical and reconnaissance military applications, millimeter-wave and infrared sensors for homeland defense, and sensor and data fusion concepts for intelligent transportation systems. While at Hughes Aircraft Company, Dr. Klein developed missile deployment strategies and sensors used in missile guidance. Here he also evaluated a variety of sensor technologies for measuring vehicle flow rates and speed, including microwave and laser radars; ultrasonic, visible and infrared machine vision, magnetic, and acoustic sensors. 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 detection.

In addition to the course text, Dr. Klein is the author of Millimeter-Wave and Infrared Multisensor Design and Signal Processing, (Artech House, 1997), which describes multisensor applications, design, and performance; and Sensor Technologies and Data Requirements for ITS (Artech House, 2001), which discusses sensor applications to traffic and transportation management systems. He collaborated with colleagues to prepare a review of data fusion methods that emphasize fusion of image features that aid object tracking using particle filtering, a Bayesian technique. It appears as �Sensor and Data Fusion: Taxonomy, Challenges, and Applications� in the Handbook on Soft Computing for Video Surveillance (Francis and Taylor, 2011). Dr. Klein received his PhD in electrical engineering from New York University in 1973. He is a past reviewer for the IEEE Transactions on Antennas and Propagation, IEEE Transactions on Geoscience and Remote Sensing, and IEEE Transactions on Aerospace and Electronic Systems.


Details:

Course: TRO-397 Duration: 3 Days FEE: $1,499 CEUs: 2.16




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Last modified October 29, 2015.