What My Students Are Saying
Course: AI in Radar Signal Processing
Note: This is a review from roughly halfway through the course.
In the course "AI in Radar Signal Processing," Dr. Regev is able to take a complicated cutting edge topic, deliver the background concepts and mathematics quickly yet thoroughly, and then delve into real applications with example code. I work professionally in the automotive radar space and I know that there are many difficult problems still to solve because it's still a new burgeoning technology. The course helped refresh some of the basics, and gave me real applicable mathematical tools to help solve some of these difficult problems using artificial neural networks and other machine learning techniques. I would highly recommend this course for those looking to add some versatile tools to their toolbox if they are working with radar or other similar technologies.
Regards,
—Lucas Wells
I have been working on radar signal processing and target tracking since the second semester of my undergraduate studies, and I am fortunate to study under one of the world's leading experts in these fields. This course provided me with a quick and effective restart in understanding radar systems, particularly in the area of mmWave radar technology.
The mathematical depth and the clear code illustrations are simply perfect, making it an essential resource for anyone looking to advance their skills. If you are an aspiring radar engineer or work in any radar-related field, this course is exactly what you need.
UNQUESTIONINGLY ATTEND THIS COURSE.
—Dayananda B N, Graduate Student, National Institute of Technology Karnataka
Dr. Regev’s AI in Radar Signal Processing course was transformative. The depth of knowledge, paired with real-world applications, helped me grasp complex concepts with ease and helped me with the current projects I’m engaged in. I highly recommend this to anyone serious about advancing their radar signal processing and AI skills.”
—Prem Kumar, Senior Technical Lead in Mercedes Benz, India
Dr. Regev's excellent course is well-structured, offering in-depth detail for anyone working in the field. The content is comprehensive, offering valuable theoretical and practical knowledge in radar signal processing. I am very excited to apply the skills I've gained from this course to more advanced radar signal processing projects. I highly recommend this course to both professionals and students interested in radar signal processing and AI.
—Michael Nelson, EE Student at Cal Poly Pomona
Practical FMCW Radar Signal Processing
Launching 01 December 2024
Course Description
Dive into the world of Frequency Modulated Continuous Wave (FMCW) radar signal processing with this hands-on course designed for engineers, researchers, and students. Through a combination of theory and practical coding exercises, you will gain a deep understanding of FMCW radar principles, waveform design, signal processing techniques, and performance analysis.
This course covers a wide range of topics, from the fundamentals of FMCW radar to advanced concepts such as stretch processing, 2D FFT for range-Doppler estimation, angle estimation using linear arrays, and CFAR detection. You will learn to design and analyze FMCW waveforms, develop link budgets, evaluate velocity and range performance, and implement sidelobe suppression techniques using window functions.
By the end of the course, you will have the skills and knowledge to design, simulate, and analyze FMCW radar systems using MATLAB or Python, preparing you for real-world applications in automotive radar, remote sensing, and more.
Course Highlights
- Hands-on learning approach with Python / MATLAB exercises and simulations
- Comprehensive coverage of FMCW radar principles and signal processing techniques
- In-depth exploration of range and Doppler estimation, angle estimation, and CFAR detection
- Practical insights into waveform design, link budget analysis, and performance evaluation
- Real-world case studies and applications of FMCW radar technology
- Expert instruction from experienced radar engineers and researchers
- Opportunity to work on a capstone project to design and analyze an FMCW radar system
- Access to course materials, including lecture slides, code, and reference resources
- Interaction with a global community of radar professionals and enthusiasts
- Certificate of completion to showcase your newly acquired skills
Whether you are a radar engineer looking to expand your expertise, a researcher exploring advanced signal processing techniques, or a student eager to learn about cutting-edge radar technology, this course provides a comprehensive and engaging learning experience. Join us and unlock the potential of FMCW radar signal processing!
Enroll Now
Don't miss this opportunity to master practical FMCW radar signal processing. Enroll now and take your skills to the next level!
Meet your instructors
✳
Meet your instructors ✳
Howdy! I'm Nir Regev, a professor and professional in artificial intelligence, with a journey spanning 26 years focused on algorithm development across various sectors.
With a Ph.D. in Electrical Engineering from Ben-Gurion University of the Negev, Israel, my career has been dedicated to exploring and advancing the fields of radar and lidar signal processing, computer vision, machine learning and AI.
My experience covers areas like multi-target tracking, radar micro-Doppler phenomena, and statistical signal processing. I apply this expertise in both industry and academia, guiding projects to fruition and sharing knowledge.
At AlephZero.ai, I lead initiatives that bridge theoretical concepts with practical applications. As an Adjunct Professor at Cal Poly Pomona in Electrical and Computer Engineering, I enjoy teaching and inspiring the next generation of technologists.
Join me in exploring the intersection of technology and innovation, where every challenge is an opportunity for growth.
What you’ll learn
Module 1: Introduction to FMCW Radar
Lesson 1.1: Principles of FMCW Radar
Lesson 1.2: Applications of FMCW Radar
Lesson 1.3: FMCW Radar Equations
Lesson 1.4: Radar Cross-Section (RCS)
Module 2: FMCW Waveform Design and Analysis
Lesson 2.1: FMCW Waveform Types
Lesson 2.2: Waveform Design Considerations. Velocity and Range Tradeoffs.
Lesson 2.3: Waveform Analysis
Module 3: Stretch Processing
Lesson 3.1: Principles of Stretch Processing
Lesson 3.2: Implementing Stretch Processing
Lesson 3.3: Advantages and Limitations of Stretch Processing
Module 4: CFAR Detection
Lesson 4.1: Principles of CFAR Detection
Lesson 4.2: CFAR Variants (CA-CFAR, OS-CFAR, etc.)
Lesson 4.3: CFAR Performance Analysis (ROC, AUC, PD, PFA and Neyman Pearson’s Theorem)
Module 5: Range and Doppler Estimation
Lesson 5.1: Range Estimation using FFT
Lesson 5.2: Doppler Estimation using FFT
Lesson 5.3: 2D FFT for Range-Doppler Processing
Module 6: Angle Estimation with Linear Arrays
Lesson 6.1: Principles of Angle Estimation
Lesson 6.2: Linear Array Architectures
Lesson 6.3: Angle Estimation Algorithms
Module 7: Link Budget Analysis
Lesson 7.1: FMCW Radar Range Equation
Lesson 7.2: Noise and Clutter Considerations
Lesson 7.3: Link Budget Calculation
Module 8: Velocity and Range Performance
Lesson 8.1: Velocity Resolution and Accuracy
Lesson 8.2: Range Resolution and Accuracy
Lesson 8.3: Factors Affecting Velocity and Range Performance
Module 9: Window Functions and Sidelobe Suppression
Lesson 9.1: Overview of Window Functions
Lesson 9.2: Sidelobe Suppression Techniques
Lesson 9.3: Implementing Window Functions
Course Project
Students will work on a hands-on project to design, simulate, and analyze an FMCW radar system. The project will involve applying the concepts and techniques learned throughout the course to a real-world scenario.
Prerequisites
Familiarity with MATLAB or Python programming
Knowledge of signal processing fundamentals
Basic knowledge of physics and mathematics
Familiarity with calculus and complex numbers
No prior experience in radar technology required
Recommended Textbooks
"Principles of Modern Radar: Volume 1, Basic Principles" by Mark A. Richards, James A. Scheer, and William A. Holm
"Radar for Fully Autonomous Driving" by Matt Markel
"Fundamentals of Radar Signal Processing" by Mark A. Richards
Course FAQ
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A: To get the most out of this course, you should have a basic understanding of radar principles, familiarity with MATLAB or Python programming, and knowledge of signal processing fundamentals. If you are unsure about your readiness, please contact our course instructors for guidance.
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A: While the course assumes some background knowledge, it is designed to be accessible to students with varying levels of experience. The course starts with the fundamentals of FMCW radar and progressively moves to more advanced topics, ensuring that all students can follow along and gain valuable insights.
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A: The course is delivered through a combination of lectures, MATLAB and Python exercises, and hands-on projects. Lecture slides, MATLAB and Python code, and reference materials will be provided to support your learning. You will have access to a virtual learning environment where you can interact with me and fellow students.
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A: The course is designed to be completed in 16 weeks. However, you will have access to the course materials for 6 months after the official end date, allowing you to revisit the content and continue your learning journey.
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A: Participants will have access to the course materials for a period of 6 months after the course completion date.
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A: Yes, participants who successfully complete the course and the associated projects will receive a certificate of completion.
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A: Absolutely! The course features interactive Q&A Discord channel where students can ask questions, seek clarifications, and engage in discussions with me and fellow students.
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A: Yes, the course is specifically designed to provide practical insights and techniques that can be directly applied to real-world radar systems.
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A: Yes, I offer special discounts for group enrollments, academic institutions and active engineering students. Please contact me for more details.
Dive into the world of Frequency Modulated Continuous Wave (FMCW) radar signal processing with this hands-on course designed for engineers, researchers, and students. Through a combination of theory and practical Python and MATLAB exercises, you will gain a deep understanding of FMCW radar principles, waveform design, signal processing techniques, and performance analysis.