Acoustics & Sonar Engineering Radar, Missiles & Defense Systems ...
Acoustics & Sonar Engineering Radar, Missiles & Defense Systems ...
Acoustics & Sonar Engineering Radar, Missiles & Defense Systems ...
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Practical Statistical Signal Processing Using MATLAB<br />
with <strong>Radar</strong>, <strong>Sonar</strong>, Communications, Speech & Imaging Applications<br />
Summary<br />
This four-day course covers signal processing<br />
systems for radar, sonar, communications, speech,<br />
imaging and other applications based on state-of-theart<br />
computer algorithms. These algorithms include<br />
important tasks such as data simulation, parameter<br />
estimation, filtering, interpolation, detection, spectral<br />
analysis, beamforming, classification, and tracking.<br />
Until now these algorithms could only be learned by<br />
reading the latest technical journals. This course will<br />
take the mystery out of these designs by introducing<br />
the algorithms with a minimum of mathematics and<br />
illustrating the key ideas via numerous examples using<br />
MATLAB.<br />
Designed for engineers, scientists, and other<br />
professionals who wish to study the practice of<br />
statistical signal processing without the headaches,<br />
this course will make extensive use of hands-on<br />
MATLAB implementations and demonstrations.<br />
Attendees will receive a suite of software source code<br />
and are encouraged to bring their own laptops to follow<br />
along with the demonstrations.<br />
Each participant will receive two books<br />
Fundamentals of Statistical Signal Processing: Vol. I<br />
and Vol. 2 by instructor Dr. Kay. A complete set of notes<br />
and a suite of MATLAB m-files will be distributed in<br />
source format for direct use or modification by the user.<br />
Instructor<br />
Dr. Steven Kay is a Professor of Electrical<br />
<strong>Engineering</strong> at the University of<br />
Rhode Island and the President of<br />
Signal Processing <strong>Systems</strong>, a<br />
consulting firm to industry and the<br />
government. He has over 25 years<br />
of research and development<br />
experience in designing optimal<br />
statistical signal processing algorithms for radar,<br />
sonar, speech, image, communications, vibration,<br />
and financial data analysis. Much of his work has<br />
been published in over 100 technical papers and<br />
the three textbooks, Modern Spectral Estimation:<br />
Theory and Application, Fundamentals of<br />
Statistical Signal Processing: Estimation Theory,<br />
and Fundamentals of Statistical Signal<br />
Processing: Detection Theory. Dr. Kay is a Fellow<br />
of the IEEE.<br />
January 9-12, 2012<br />
Laurel, Maryland<br />
$2095 (8:30am - 4:00pm)<br />
"Register 3 or More & Receive $100 00 each<br />
Off The Course Tuition."<br />
Course Outline<br />
1. MATLAB Basics. M-files, logical flow, graphing,<br />
debugging, special characters, array manipulation,<br />
vectorizing computations, useful toolboxes.<br />
2. Computer Data Generation. Signals, Gaussian<br />
noise, nonGaussian noise, colored and white noise,<br />
AR/ARMA time series, real vs. complex data, linear<br />
models, complex envelopes and demodulation.<br />
3. Parameter Estimation. Maximum likelihood, best<br />
linear unbiased, linear and nonlinear least squares,<br />
recursive and sequential least squares, minimum mean<br />
square error, maximum a posteriori, general linear model,<br />
performance evaluation via Taylor series and computer<br />
simulation methods.<br />
4. Filtering/Interpolation/Extrapolation. Wiener,<br />
linear Kalman approaches, time series methods.<br />
5. Detection. Matched filters, generalized matched<br />
filters, estimator-correlators, energy detectors, detection<br />
of abrupt changes, min probability of error receivers,<br />
communication receivers, nonGaussian approaches,<br />
likelihood and generalized likelihood detectors, receiver<br />
operating characteristics, CFAR receivers, performance<br />
evaluation by computer simulation.<br />
6. Spectral Analysis. Periodogram, Blackman-Tukey,<br />
autoregressive and other high resolution methods,<br />
eigenanalysis methods for sinusoids in noise.<br />
7. Array Processing. Beamforming, narrowband vs.<br />
wideband considerations, space-time processing,<br />
interference suppression.<br />
8. Signal Processing <strong>Systems</strong>. Image processing,<br />
active sonar receiver, passive sonar receiver, adaptive<br />
noise canceler, time difference of arrival localization,<br />
channel identification and tracking, adaptive<br />
beamforming, data analysis.<br />
9. Case Studies. Fault detection in bearings, acoustic<br />
imaging, active sonar detection, passive sonar detection,<br />
infrared surveillance, radar Doppler estimation, speaker<br />
separation, stock market data analysis.<br />
What You Will Learn<br />
• To translate system requirements into algorithms that<br />
work.<br />
• To simulate and assess performance of key<br />
algorithms.<br />
• To tradeoff algorithm performance for computational<br />
complexity.<br />
• The limitations to signal processing performance.<br />
• To recognize and avoid common pitfalls and traps in<br />
algorithmic development.<br />
• To generalize and solve practical problems using the<br />
provided suite of MATLAB code.<br />
58 – Vol. 109 Register online at www.ATIcourses.com or call ATI at 888.501.2100 or 410.956.8805
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