Practical Statistical Signal Processing Using MATLAB

Course length:

4 Days


$2,495.00 excl.

Course dates

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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 using MATLAB.

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.

Who Should Attend:

Scientists, engineers, and managers involved in the management, planning, design, fabrication, integration, test, or operation of space instruments, space subsystems, and spacecraft. The course will provide an understanding of the space subsystems and disciplines necessary to develop a space instrument and spacecraft and the systems engineering approach to integrate these into a successful mission.

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 code.

Course Outline:

  1. Matlab Basics — M-files, logical flow, graphing, debugging, special characters, array manipulation, vectorizing computations, useful toolboxes.
  2. 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.
  3. 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.
  4. Filtering/Interpolation/Extrapolation — Wiener, linear Kalman approaches, time series methods.
  5. 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 simulation.
  6. Spectral Analysis — Periodogram, Blackman-Tukey, autoregressive and other high resolution methods, eigenanalysis methods for sinusoids in noise.
  7. Array Processing — Beamforming, narrowband vs. wideband considerations, space-time processing, interference suppression.
  8. 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.
  9. 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.


Dr. Steven Kay is a Professor of Electrical Engineering at the University of Rhode Island and the President of Signal Processing Systems, a consulting firm to industry and the government. He has over 25 years of research and development experience in designing optimal statistical signal processing algorithms for radar, sonar, speech, image, communications, vibration, and financial data analysis. Much of his work has been published in over 100 technical papers and the three textbooks, Modern Spectral Estimation: Theory and Application, Fundamentals of Statistical Signal Processing: Estimation Theory and Fundamentals of Statistical Signal Processing: Detection Theory. Dr. Kay is a Fellow of the IEEE.