Development of Statistical Signal Processing Algorithms Training

Development of Statistical Signal Processing Algorithms Training

Introduction:

Development of Statistical Signal Processing Algorithms Training Course Description

This 3-day Development of Statistical Signal Processing Algorithms Training covers the fundamental approaches to developing statistical signal processing algorithms to meet system design specifications. Additionally, the algorithms that are currently used in practice and have stood the “test of time” are highlighted. The methodology to design, evaluate, and test new algorithms is presented in a simple step-by-step manner. In doing so, the computer language MATLAB is utilized. All algorithms and methods discussed have been implemented in MATLAB and will be provided to the attendee. The course is designed for engineers, scientists, and other persons who wish to implement and/or design statistical signal processing algorithms without having to scour the current literature for possible solutions. The presentations will emphasize actual working algorithms and will deemphasize the mathematics behind them so that the course will be accessible to those who may not be familiar with the theoretical foundations. Knowledge of the instructor’s previous books, “Modern Spectral Estimation”, “Fundamentals of Statistical Signal Processing: Estimation” and “Fundamentals of Statistical Signal Processing: Detection” is not required. Attendees are encouraged to bring their laptops so that they are able to exercise the programs along with the instructor. All MATLAB source code will be provided for course and future use. Each participant will receive the recently released book “Fundamentals of Statistical Signal Processing, Vol. III, Practical Algorithm Development”, by Steven Kay, Prentice-Hall, 2013. The book contains a CD with the MATLAB programs.

Development of Statistical Signal Processing Algorithms TrainingRelated Courses:

Duration:3 days

Skills Gained:

• Step by step approach to the design of algorithms
• Comparing and choosing signal and noise models
• Performance evaluation, metrics, tradeoffs, testing, and documentation
• Optimal approaches using the ‘big theorems”
• Algorithms for estimation, detection, and spectral estimation
• Lessons learned and “rules of thumb” for each topic
• Complete case studies

Customize It:

With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.

Course Content:

Methodology for algorithm design: flow charts, example of algorithm design

Mathematical modeling of signals: linear vs. nonlinear, deterministic signals, random signals, unknown parameters

Mathematical modeling of noise: white Gaussian noise, colored Gaussian noise, general Gaussian noise, IID nonGaussian noise

Signal model selection: flow charts, random processes, transients, periodic models, model order estimation

Noise model selection: flow charts, estimation of probability density functions, spectrum, moments, covariance matrix, model order estimation, confidence intervals

Performance, evaluation and testing: metrics, Monte Carlo evaluations, bias versus variance, mean square error, probability of error, receiver operating characteristics, software development, documentation

Optimal approach using the big theorems: Neyman-Pearson, likelihood ratio, maximum likelihood, maximum a posterior, minimum MSE, linear models

Specific algorithms for estimation, detection, and spectral estimation: parameter estimation, signal extraction, adaptive filtering, sinusoidal estimation, matched filters, estimator-correlator, spectral estimation via Fourier and high resolution methods

Complex data extensions: complex demodulation, complex random variables and random processes, extensions of all algorithms to complex data

Case studies: Radar Doppler center frequency estimation, magnetic signal detection, and heart rate monitoring

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Time Frame: 0-3 Months4-12 Months

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