Practical Statistical Signal Processing — using MATLAB
$2295 per person
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.
Please bring a laptop with a USB port to this class.
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.
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