Radar Signal Analysis & Processing with MATLAB Training

Radar Signal Analysis & Processing with MATLAB Training

Course Delivery

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Course Overview:

Radar Signal Analysis & Processing with MATLAB Training Course Description

This three-day Radar Signal Analysis & Processing with MATLAB Training develops the technical background needed to predict and understand the factors controlling the performance of radar systems including anti-clutter and anti-jamming signal processing techniques. The Radar Signal Analysis & Processing with MATLAB Training course introduces the fundamental concepts and properties of various techniques without the necessity of a detailed analytic background. Each student will receive the instructor’s textbook MATLAB Simulations for Radar Systems Design as well as course notes.

Related Courses:

Radar- Principles Training
Radar 201 Training

Customize It:

• We can adapt this Radar Signal Analysis & Processing with MATLAB Training course to your group’s background and work requirements at little to no added cost.
• If you are familiar with some aspects of this Radar Signal Analysis & Processing with MATLAB Training course, we can omit or shorten their discussion.
• We can adjust the emphasis placed on the various topics or build the Radar Signal Analysis & Processing with MATLAB Training course around the mix of technologies of interest to you (including technologies other than those included in this outline).
• If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Radar Signal Analysis & Processing with MATLAB Training course in manner understandable to lay audiences.

Radar Signal Analysis & Processing with MATLAB Training – Objectives:

Upon completing this Radar Signal Analysis & Processing with MATLAB Training course, learners will be able to meet these objectives:

• Learn radar theory and signal processing concepts.
• Learn that the detection range in thermal or jamming noise depends primarily on the amount of energy transmitted and not upon the waveform parameters, such as bandwidth, etc. but the waveform determines detection range in clutter.
• Learn that Constant False Alarm Rate (CFAR) is mandatory and how signal processing is used to emphasize the desired signal and reduce the response to clutter and jamming.
• The design of radar systems is a constant trade-off as increasing the goodness of one parameter, such as resolution, always causes degradation of another parameter. From this course you will learn evaluation criteria to aid in choosing desirable choices. course notes.

Radar Signal Analysis & Processing with MATLAB Training – Course Syllabus:

Radar System Fundamentals.

Target Detection — Resolution and clutter.

Maximum Detection — Range in noise, targets in clutter, jamming and clutter.

Horizon and Multipath — Effects on detection range.

False Alarm — Probability effects, sensitivity and cfar processors.

System Parameter — Interrelations.

Transmit/Receive Antennas.

System Performance Equations.

Resolution — Measurement accuracy and ambiguity.

Tracking Radar Techniques.

Waveforms and Matched Filtering.

Very Wideband Lfm Waveforms.

Reflector and Phased Array Antennas.

Sidelobe Reduction — Weighting, effects of errors on sidelobe reduction, and earth effects on antenna patterns.

Doppler Signal Processing — Stagger coded mti waveforms, implementation errors effects, A/d converters, effect of a/d converters on detection, special doppler processing for airborne radars.

Sidelobe Canceller (Slc) — Adaptive algorithms, constant false alarm rate (cfar) processor, multiple sidelobe cancellers (mslc), Optimum array and doppler processing.

Modern Spectral Estimation and Super Resolution.

Instructor

Dr. Andy Harrison is a Systems Analyst at Delta Research. He has extensive experience in the testing, simulation and analysis of radar systems and subsystems. Dr. Harrison also has experience in the development and testing of advanced radar algorithms, including track correlation and SAR imaging. Dr. Harrison led the utilization and anchoring of open source radar models and simulations for integration into end-to-end simulations. Responsibilities included development of tools for radar simulation and visualization of radar operational scenarios. Dr. Harrison has also developed genetic algorithm and particle swarm algorithms for the adaptive nulling and pattern correction of phased array antennas, and serves as an associate editor for the Applied Computational Electromagnetics Society.

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