ATI's Practical Statistical Signal Processing
— using MATLAB
This 4-day course covers signal processing systems
for radar, sonar, communications, speech, imaging
and other applications based on state-of-the-art
computer algorithms. These algorithms include
important tasks such as data simulation, parameter
estimation, filtering, interpolation, detection, spectral
analysis, beamforming, classification, and tracking.
Until now these algorithms could only be learned by
reading the latest technical journals. This course will
take the mystery out of these designs by introducing
the algorithms with a minimum of mathematics and
illustrating the key ideas via numerous examples
Designed for engineers, scientists, and other
professionals who wish to study the practice of
statistical signal processing without the headaches,
this course will make extensive use of hands-on
MATLAB implementations and demonstrations.
Attendees will receive a suite of software source code
and are encouraged to bring their own laptops to
follow along with the demonstrations.
Each participant will receive a book,
Fundamentals of Statistical Signal Processing: Vol. I by instructor Dr. Kay. A complete set of
notes and a suite of MATLAB m-files will be
distributed in source format for direct use or
modification by the user.
Read excerpt from Dr. Kay's book Fundamentals of Statistical Signal Processing
View course sampler
An Essay On Model Based Classification Using Multi-Ping Data, by Dr. Steven Kay
View the companion video here
What you will learn:
- To translate system requirements into algorithms that work.
- To simulate and assess performance of key algorithms.
- To tradeoff algorithm performance for computational complexity.
- The limitations to signal processing performance.
- To recognize and avoid common pitfalls and traps in algorithmic development.
- To generalize and solve practical problems using the provided suite of
- Matlab Basics -- M-files, logical flow, graphing, debugging,
special characters, array manipulation, vectorizing computations, useful
- Computer Data Generation -- Signals, Gaussian noise, nonGaussian
noise, colored and white noise, AR/ARMA time series, real vs. complex data,
linear models, complex envelopes and demodulation.
- Parameter Estimation -- Maximum likelihood, best linear unbiased,
linear and nonlinear least squares, recursive and sequential least squares,
minimum mean square error, maximum a posteriori, general linear model,
performance evaluation via Taylor series and computer simulation methods.
- Filtering/Interpolation/Extrapolation -- Wiener, linear Kalman
approaches, time series methods.
- Detection -- Matched filters, generalized matched filters,
estimator-correlators, energy detectors, detection of abrupt changes, min
probability of error receivers, communication receivers, nonGaussian
approaches, likelihood and generalized likelihood detectors, receiver
operating characteristics, CFAR receivers, performance evaluation by computer
- Spectral Analysis -- Periodogram, Blackman-Tukey, autoregressive
and other high resolution methods, eigenanalysis methods for sinusoids in
- Array Processing -- Beamforming, narrowband vs. wideband
considerations, space-time processing, interference suppression.
- Signal Processing Systems -- Image processing, active sonar
receiver, passive sonar receiver, adaptive noise canceler, time difference of
arrival localization, channel identification and tracking, adaptive
beamforming, data analysis.
- Case Studies -- Fault detection in bearings, acoustic imaging,
active sonar detection, passive sonar detection, infrared surveillance, radar
Doppler estimation, speaker separation, stock market data analysis.
Tuition for this four-day course is $2295 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 firstname.lastname@example.org.