3Year National Science Foundation Project NSF0410564

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3-Year National Science Foundation Project
NSF-0410564
Hands-On Interdisciplinary Laboratory Program An
Approach to Strengthen the Weather Radar
Curriculum
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People

• Dr. Mark Yeary
• Electrical Computer Engineering
• Dr. Tian Yu
• Electrical Computer Engineering
• Dr. Robert Palmer
• School of Meteorology
• Dr. Mike Biggerstaff
• School of Meteorology
• Dr. L. Dee Fink
• Instructional Development Program

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• A unique federal, private, state and academic
  partnership will develop the phased array radar
  technology. Participants include
• NOAA's National Severe Storms Laboratory and
  National Weather Service Radar Operations Center
• Lockheed Martin
• U.S. Navy
• University of Oklahoma's School of Meteorology
  and School of Electrical and Computer Engineering
• Oklahoma State Regents for Higher Education
• Federal Aviation Administration
• Basic Commerce and Industries

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Hands-On Interdisciplinary Laboratory Program A
Peer Teacher Approach to Strengthen the Weather
Radar Curriculum

• 4 professors
• 6 students hired
• 840 students impacted
• 3 year project

This project offers the development of a
revolutionary laboratory and coursework
curriculum that coincides with the
interdisciplinary development and integration of
the School of Electrical and Computer Engineering
and the School of Meteorology.
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Laboratory Modules

• Data collection developing different scanning
  patterns
• Data processing computing and enhanced
  algorithms to extract weather information from
  the raw radar data
• Data display placing the composite weather
  information on a user-friendly computer display
• Data interpretation scientific understanding and
  discovery of the displayed data -- this includes
  the locations and dynamics of storms,
  precipitation, tornados, downbursts, and the like.

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Strengthening the Weather Radar Curriculum
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Other New Innovations

• Courses
• Introduction to Meteorology
• introduces students to important phenomena and
  physical processes that occur in the Earth's
  atmosphere. Through lectures and laboratory
  exercises, students will learn the basic concepts
  and tools that are used to study atmospheric
  problems. (METR, soph, fall and spring semesters)
• Electromagnetic Fields
• is an existing course in which modifications are
  currently being explored and implemented which
  include plane wave propagation, polarization,
  reflection, and an introduction to
  radiation/antennas all related to the study of
  the atmosphere. (ECE, junior, spring semesters)
• Introduction to Measurement Systems
• introduces the physical principles of
  meteorological sensors, discusses static and
  dynamic performance concepts, and explores the
  concepts of meteorological measurement systems.
  (METR, junior, fall semesters)
• Radar Engineering
• introduces various radar system designs and their
  applications with an emphasis on weather radar.
  Radar system architecture and their
  functionalities and limitations of subsystems are
  discussed. It is a senior level course and is
  cross listed in both schools. (ECE, senior, fall
  semesters)
• Radar Meteorology
• is an established course (that has been updated
  with new laboratory experiments) that develops
  the quantitative relationships between a radar
  and its target i.e., interpretation of the
  data. It is a senior level course and advertised
  for enrollment in both schools. (METR, senior,
  spring semesters)
• Weather Radar Theory and Practice
• is a new course (with supporting laboratory
  experiments) that concentrates on the radar
  equation, time domain algorithms, and spectral
  analysis. It is a senior level course and is
  available to students in both departments. (METR,
  senior, fall semesters)
• Adaptive Digital Signal and Array Processing
• is a new course devoted to the theory of adaptive
  algorithms for aircraft tracking and the
  discovery of interesting weather targets . (ECE,
  senior, fall semesters)

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Other New Innovations

• Undergraduate Peer Teachers
• Rather than employing 1 or 2 traditional graduate
  students to aid in the laboratory exercises and
  research, 6 undergraduate students are budgeted.
• These highly motivated students will be very
  familiar with the radar and image processing
  based PAR research agenda.

Matrix Clutter Filters for Phased Array Radars
Following the work of Urkowitz and Owen (1998),
we have experimented with clutter mitigation
techniques for agile-beam radars
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New Course Radar Engineering

• Course Description Introducing radar
  fundamentals including
• radar systems
• signal statistics
• signal processing
• Students will learn the concepts and theories of
  algorithms in class. They will work on three
  well-designed team projects to implement those
  algorithms and present their results.
• Moreover, a final project is given to challenge
  the students.

Offered as ECE 5973 (graduate level) in the
spring semesters. Also open to qualified
undergraduates.
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New Course Adaptive Digital Signal and Array
Processing

• As processing power continues to increase (via
  large computers or compact digital signal
  processors), the ability to expand the presence
  adaptive filtering will also flourish as the
  quintessential tool for harnessing more
  information from time-varying noisy signals.
• The students are provided a solid foundation of
  adaptive algorithms and learn about the adaptive
  filter design process.

• Offered each fall.

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Target Tracking Emerging Non-Linear Stochastic
State Estimation Techniques
Networked tracking algorithms based on particle
filtering will provide improvements to these
areas that require the analysis of dynamic
movements, particularly when the underlying
dynamic models are non-linear and when the
measurement noise is non-Gaussian. As discussed
in the most recent literature, particle filtering
is defined as an emerging Bayesian method based
on sequential Monte-Carlo non-linear state
estimation techniques.
Left PAR detections made in a 90 degree sector
looking towards the OKC airport, Sept 15, 2005.
Right Specific tracks are formed for data
collected Sept 19, 2005.

• Why is particle filtering important for PAR?
• Tracking in the presence of strong maneuvers. As
  seen above, when the object denoted by the green
  curve makes a sharp change in course,
  conventional techniques (such as the Extended
  Kalman Filter) cannot maintain track.
• One step ahead prediction algorithms for optimum
  beam-steering

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Neural Networks Adaptive Algorithms in Practice
for Advanced Decision Making for Weather Radar
Signals
Adaptive algorithms
The Doppler spectrum reveals the distribution of
velocities within the radar volume.
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Course Radar and Mesoscale Meteorology

• Radar theory
• Applications of radar technology for the
  meteorologist, scientist, and engineer
• Wind retrieval, storm structure, estimate
  rainfall
• 40 students per year
• 4 hour course

• Offered as METR 4624 each spring.

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Looking forward