ATI's Multi-Sensor Correlation & Kalman Filtering course

Summary: In recent years, tremendous strides have been made in the improvement of existing and the development of new, more powerful, sensor systems. The result is a tidal wave of data which threatens to overwhelm, rather than assist, today's warfighter. The process of automatically filtering, aggregating and extracting the desired information from multiple sensors and sources is an emerging technology, commonly referred to as Data Fusion. The objective of this course is to introduce engineers, scientists, managers, and military operations personnel to the field of data fusion and to the key technologies which are available today for application to this field. The course is designed to be rigorous where appropriate, while remaining accessible to students without a specific scientific background in this field. This course will identify and characterize the principal components of typical data fusion systems. A variety of techniques for addressing different aspects of the data fusion problem will be described. For example, different techniques are required for the assimilation of "time-late" data than those used for "real-time" data. Real world examples of data fusion systems used by both the Navy and the Air Force will be presented and discussed. This course will also use specific illustrative examples to show the tradeoffs and systems issues between application of different techniques. Specific examples to be discussed include correlation of radar data from multiple platforms and correlation of off-board bearings-only type data, such as sonar detections from a LAMPS Mk III/ SH-60R helicopter, with on-board detections. Instructor: Dr. C. Allen Butler is a recognized expert in the field of Multi-Sensor Correlation and Multi-Target Tracking, with over ten years' experience in both theoretical and engineering development of correlation and tracking systems. As a senior associate with D. H. Wagner Associates, he has worked on the development of several advanced Navy, Air Force, and Army data fusion systems. Navy systems include (1) the Global Correlation Engine (GCE), a multiple hypothesis/non-Gaussian data fusion system enhanced for use in the SH-60R Decision Support System (DSS), and (2) the Navy's Enhanced Radar Distribution and Display System (RADDS). He is the Project Manager for the Multiple Sensor Statistical Likelihood Estimator (MUSSLE), which was designed to replace the data fusion system onboard the E-3 AWACS. He recently developed and implemented new algorithms for optimal target search and optimal resource allocation for a Counter-narcotics application and in the Navy's Anti-Surface Warfare Tactical Decision Aid (ASUWTDA). Dr. Butler recently served on the Navy's Multi-Sensor Integration System Engineering Team, and participated in a congressionally mandated study on MTI/IMINT fusion. Contact this instructor (please mention course name in the subject line) What you will learn: Classical Kalman Filtering Techniques and their role in the Data Fusion Architecture Multiple Hypothesis Tracking and Correlation: Advantages and Limitations Contact-to-Track & Track-to-Track Architectures: Advantages and Disadvantages Non-Gaussian Data Fusion Methods - Monte Carlo Tracking Sensor Characteristics - Impact on Algorithm Selection Identification/Classification Technologies: Expert Systems, Neural Networks, Fuzzy Logic Real World Data Fusion Systems and Implementation Issues Course Outline: Introduction and Overview— Background & Course Outline, The Need For Data Fusion Single-Target Single-Sensor Tracking—State Estimation & Kalman FilteringAlpha-Beta Filtering Bearings-Only Tracking Multiple-Target Single-Sensor Tracking—Data Association Problem Joint Probability Data Association Multiple Hypothesis Tracking. MATCH - A Real World Example Multiple-Target Multiple-Sensor Tracking—Fusion Architectures - Centralized vs. Distributed, Fusion Architectures - Contact-to-track & Track-to-track,Data Registration (Gridlock) Covariance Intersection Method. Non-Gaussian Tracking—Markov Methods, Monte Carlo Tracking, Global Correlation Engine - Example 2 Sensor Modeling—ESM Sensors, Acoustic Sensors, Image Sensors, Sensor Anomalies Information Fusion—Identification/Classification, Bayesian Inference, Dempster Shafer (Evidential Reasoning), Rule-Based & Expert Systems, Neural Networks, Fuzzy Logic System Evaluation—Kinematic Performance, Correlation Scoring, Identity & Classification Performance, Real-Time Requirements; Scalability Tuition: Tuition for this four-day course is $1495 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 410-956-8805 or send an email to ati@ATIcourses.com. Register Now Without Obligation