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Radar Systems Analysis & Design Using MATLAB Training Course Description
This Radar Systems Analysis & Design Using MATLAB Training provides a comprehensive description of radar systems analyses and design. A design case study is introduced and as the material coverage progresses throughout the course, and new theory is presented, requirements for this design case study are changed and / or updated, and the design level of complexity is also increased. This design process is supported with a comprehensive set of MATLAB-7 code developed for this purpose. By the end, a comprehensive design case study is accomplished. This will serve as a valuable tool to radar engineers in helping them understand radar systems design process. Each student will receive the instructor’s textbook MATLAB Simulations for Radar Systems Design as well as course notes.
• We can adapt this Radar Systems Analysis & Design Using 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 Systems Analysis & Design Using MATLAB Training course, we can omit or shorten their discussion.
• We can adjust the emphasis placed on the various topics or build the Radar Systems Analysis & Design Using 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 Systems Analysis & Design Using MATLAB Training course in manner understandable to lay audiences.
Radar Systems Analysis & Design Using MATLAB Training – Objectives:
Upon completing this Radar Systems Design & Engineering Training course, learners will be able to meet these objectives:
• How to select different radar parameters to meet specific design requirements.
• Perform detailed trade-off analysis in the context of radar sizing, modes of operations, frequency selection, waveforms and signal processing.
• Establish and develop loss and error budgets associated with the design.
• Generate an indepth understanding of radar operations and design philosophy.
• Several mini design case studies pertinent to different radar topics will enhance understanding of radar design in the context of the material presented.
Radar Systems Analysis & Design Using MATLAB Training – Course Syllabus:
• Radar Basics: Radar Classifications; Range; Range Resolution; Doppler Frequency; The Radar Equation; Radar Reference Range; Search (Surveillance); Pulse Integration; Detection Range with Pulse Integration; Radar Losses; Range and Doppler Ambiguities; Resolving Range Ambiguity; Resolving Doppler Ambiguity; “MyRadar” Design Case Study – Visit 1.
• Radar Detection: Detection in the Presence of Noise; Probability of False Alarm; Probability of Detection; Coherent Integration; Non-Coherent Integration; Detection of Fluctuating Targets; Threshold Selection; Probability of Detection Calculation; Detection of Swerling Targets; The Radar Equation Revisited; “MyRadar” Design Case Study – Visit 2.
• Radar Waveforms: Low Pass, Band Pass Signals and Quadrature Components; The Analytic Signal; CW and Pulsed Waveforms; Linear Frequency Modulation Waveforms; High Range Resolution; Stepped Frequency Waveforms; Range Resolution and Range Ambiguity; Effect of Target Velocity; The Matched Filter; Matched Filter Response to LFM Waveforms; Waveform Resolution and Ambiguity; “Myradar” Design Case Study – Visit 3.
• The Radar Ambiguity Function: Examples of the Ambiguity Function; Single Pulse Ambiguity Function; LFM Ambiguity Function; Coherent Pulse Train Ambiguity Function; Ambiguity Diagram Contours; Digital Coded Waveforms; Frequency Coding (Costas Codes); Binary Phase Codes; Pseudo-Random (PRN) Codes; “MyRadar” Design Case Study -Visit 4.
• Pulse Compression: Time-Bandwidth Product; Radar Equation with Pulse Compression; LFM Pulse Compression; Correlation Processor; Stretch Processor; “MyRadar” Design Case Study – Visit 5.
• Surface and Volume Clutter: Clutter Definition; Surface Clutter; Radar Equation for Area Clutter – Airborne Radar; Radar Equation for Area Clutter – Ground Based Radar; Volume Clutter; Radar Equation for Volume Clutter; Clutter Statistical Models; “MyRadar” Design Case Study – Visit 6.
• Phased Arrays: Directivity, Power Gain, and Effective Aperture; Near and Far Fields; General Arrays; Linear Arrays; Array Tapering; Computation of the Radiation Pattern via the DFT; Planar Arrays; Array Scan Loss; “MyRadar” Design Case Study – Visit 7.
• Electronic Countermeasures: Jammers; Self-Screening Jammers (SSJ); Stand-Off Jammers (SOJ); Range Reduction Factor; Chaff.
• Radar Cross Section (RCS): RCS Definition; RCS Prediction Methods; Dependency on Aspect Angle and Frequency; RCS Dependency on Polarization; Polarization; RCS of Simple Objects; Sphere; Ellipsoid; Circular Flat Plate; Truncated Cone (Frustum); Cylinder; Rectangular Flat Plate; Triangular Flat Plate.
• Radar Wave Propagation (time permitting): Earth Atmosphere; Refraction; Stratified Atmospheric Refraction Model; Four-Third Earth Model; Ground Reflection; Smooth Surface Reflection Coefficient; Rough Surface Reflection; Total Reflection Coefficient; The Pattern Propagation Factor; Flat Earth; Spherical Earth. This course will serve as a valuable source to radar system engineers and will provide a foundation for those working in the field who need to investigate the basic fundamentals in a specific topic. It provides a comprehensive day-to-day radar systems design reference.
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.