Systems Engineering - ATI

Multi-Target Tracking and Multi-Sensor Data Fusion
February 1-3, 2011
Beltsville, Maryland
$1590 (8:30am - 4:00pm)
"Register 3 or More & Receive $100 00 each
Off The Course Tuition."
Instructor
Revised With
Newly Added
Topics
Summary
The objective of this course is to introduce
engineers, scientists, managers and military
operations personnel to the fields of target
tracking and 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. The
course will start from the fundamentals and
move to more advanced concepts. This course
will identify and characterize the principle
components of typical tracking systems. A
variety of techniques for addressing different
aspects of the data fusion problem will be
described. Real world examples will be used
to emphasize the applicability of some of the
algorithms. Specific illustrative examples will
be used to show the tradeoffs and systems
issues between the application of different
techniques.
Stan Silberman is a member of the Senior
Technical Staff at the Johns Hopkins Univeristy
Applied Physics Laboratory. He has over 30
years of experience in tracking, sensor fusion,
and radar systems analysis and design for the
Navy,Marine Corps, Air Force, and FAA.
Recent work has included the integration of a
new radar into an existing multisensor system
and in the integration, using a multiple
hypothesis approach, of shipboard radar and
ESM sensors. Previous experience has
included analysis and design of multiradar
fusion systems, integration of shipboard
sensors including radar, IR and ESM,
integration of radar, IFF, and time-difference-ofarrival
sensors with GPS data sources.
Course Outline
1. Introduction.
2. The Kalman Filter.
3. Other Linear Filters.
4. Non-Linear Filters.
5. Angle-Only Tracking.
6. Maneuvering Targets: Adaptive Techniques.
7. Maneuvering Targets: Multiple Model
Approaches.
8. Single Target Correlation & Association.
9. Track Initiation, Confirmation & Deletion.
10. Using Measured Range Rate (Doppler).
11. Multitarget Correlation & Association.
12. Probabilistic Data Association.
13. Multiple Hypothesis Approaches.
14. Coordinate Conversions.
15. Multiple Sensors.
16. Data Fusion Architectures.
17. Fusion of Data From Multiple Radars.
18. Fusion of Data From Multiple Angle-Only
Sensors.
19. Fusion of Data From Radar and Angle-Only
Sensor.
20. Sensor Alignment.
21. Fusion of Target Type and Attribute Data.
22. Performance Metrics.
What You Will Learn
• State Estimation Techniques – Kalman Filter,
constant-gain filters.
• Non-linear filtering – When is it needed Extended
Kalman Filter.
• Techniques for angle-only tracking.
• Tracking algorithms, their advantages and
limitations, including:
- Nearest Neighbor
- Probabilistic Data Association
- Multiple Hypothesis Tracking
- Interactive Multiple Model (IMM)
• How to handle maneuvering targets.
• Track initiation – recursive and batch approaches.
• Architectures for sensor fusion.
• Sensor alignment – Why do we need it and how do
we do it
• Attribute Fusion, including Bayesian methods,
Dempster-Shafer, Fuzzy Logic.
12 – Vol. 104 Register online at www.ATIcourses.com or call ATI at 888.501.2100 or 410.956.8805