Practical Statistical Signal Processing Using MATLAB Training

Practical Statistical Signal Processing Using MATLAB Training

Introduction:

Practical Statistical Signal Processing Using MATLAB Training Course Description

This 4-day Practical Statistical Signal Processing Using MATLAB Training 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.

Practical Statistical Signal Processing Using MATLAB TrainingRelated Courses:

Duration:4 days

Skills Gained:

• 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.

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:

Matlab Basics — M-files, logical flow, graphing, debugging, special characters, array manipulation, vectorizing computations, useful toolboxes.

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 simulation.

Spectral Analysis — Periodogram, Blackman-Tukey, autoregressive and other high resolution methods, eigenanalysis methods for sinusoids in noise.

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.

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Time Frame: 0-3 Months4-12 Months

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