AI in Radar Signal Processing Course
Launched Oct. 1st 2024
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
AI in Radar Signal Processing: Second Cohort
Course Summary: AI in Radar Signal Processing
Discover the transformative power of artificial intelligence in radar technology with our comprehensive online course, "AI in Radar Signal Processing: Enhancing Detection and Classification." This 10-hour course is designed for radar engineers, researchers, and AI enthusiasts who want to stay at the forefront of this rapidly evolving field.
Throughout the course, you'll explore the fundamentals of machine learning, deep learning architectures tailored for radar data analysis, and the integration of AI in real-time radar systems. You'll gain hands-on experience with tools like TensorFlow, PyTorch, and Scikit-learn, and work with real-world case studies to understand the practical applications of AI in radar technology.
Our expert instructors will guide you through advanced topics such as micro-Doppler phenomenon and its applications in target classification, including drones, bicyclists, and pedestrians. You'll also have the opportunity to participate in interactive Q&A sessions, live demonstrations, and coding workshops to reinforce your learning.
By the end of the course, you'll be equipped with the knowledge and skills needed to develop and optimize AI algorithms for radar signal processing, recognize challenges and limitations, and identify future research directions in this exciting field.
Enroll now and take your radar signal processing expertise to the next level with the power of artificial intelligence!
Course Highlights:
- 10 hours of comprehensive learning
- 6 modules covering AI in radar signal processing
- Hands-on workshops and demonstrations
- Real-world case studies and practical applications
- Interactive Q&A sessions with me
- Comprehensive resource material, including datasets and code snippets
Meet your instructors
✳
Meet your instructors ✳
Dr. Nir Regev
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 Radar Signal Processing and AI (1 hour)
Brief history of radar technology and its evolution.
Overview of modern radar technologies: FMCW, Pulse Doppler, and others.
Introduction to AI in radar: potential, challenges, and limitations.
Key concepts in signal processing and deep learning relevant to radar.
Module 2: Fundamentals of Machine Learning (1 hour)
Review of basic machine learning concepts (supervised, unsupervised, reinforcement).
Introduction to neural networks: architecture, training, and inference.
Tools and libraries overview: TensorFlow, PyTorch, Scikit-learn.
Module 3: Deep Learning for Radar Data Analysis (2 hours)
Preprocessing radar data for deep learning.
Architectures suitable for sequence and time-series data (CNN, RNN, LSTM).
Architectures suitable for radar image data (CNN).
Case study: Target detection using CNNs.
Challenges and limitations of applying AI in radar systems.
Module 4: Enhancing Radar Signal Processing with AI (2 hours)
Feature extraction and dimensionality reduction techniques.
Integration of AI in real-time radar systems.
Case study: AI-driven radar systems in autonomous vehicles or weather monitoring.
Module 5: Advanced Topics in AI-driven Radar Systems (2 hours)
micro-Doppler phenomenon in Radar
Case study 1: Classification of drones
Case study 2: classification of bicyclist vs. pedestrian
Case study 3: Who’s in front of the radar?
Future directions and open research problems.
Session 6: Practical Workshop on AI in Radar (2 hours)
Hands-on session using MATLAB and Python for simulating radar data and applying AI.
Building and training a small model to classify radar targets.
Performance evaluation and optimization techniques.
Discussion on computational complexity, data requirements, and interpretability.
Course Features
Interactive Q&A: Regular intervals for addressing complex questions and clarifications.
Live Demonstrations: Real-time coding and algorithm adjustment sessions.
Resource Material: Provision of datasets, code snippets, and reading material for further learning.
Learning Outcomes
Understand the application of deep learning in radar signal processing.
Develop and optimize algorithms using AI techniques for real-time radar data analysis.
Gain hands-on experience in integrating AI with radar systems.
Recognize the challenges, limitations, and future directions of AI in radar technology.
Course FAQ
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A: Participants should have a basic understanding of radar principles, signal processing, and programming languages such as MATLAB or Python. Familiarity with machine learning concepts is helpful but not mandatory.
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A: While the course is designed for radar engineers and AI enthusiasts, it includes introductory modules that cover the fundamentals of both radar and AI. However, a basic understanding of radar principles and programming is recommended.
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A: The course will primarily use MATLAB and Python, along with libraries such as TensorFlow, PyTorch, and Scikit-learn for implementing AI algorithms.
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A: Yes, all course materials, including recorded lectures, datasets, and code snippets, will be available for participants to access and refer to even after the live sessions.
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A: Yes, there will be hands-on workshops and case studies that allow participants to apply the learned concepts to real-world problems. These projects will help reinforce the understanding of AI techniques in radar signal processing.
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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 sessions where participants can ask questions, seek clarifications, and engage in discussions with the instructors and fellow learners.
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A: Yes, the course is designed to provide practical insights and techniques that can be directly applied to real-world radar systems. The case studies and hands-on workshops will help you understand how to integrate AI into your radar projects.
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A: Yes, we offer special discounts for group enrollments and academic institutions. Please contact our sales team for more information on group discounts and academic pricing.
Discover the transformative power of artificial intelligence in radar technology with our comprehensive online course, "AI in Radar Signal Processing: Enhancing Detection and Classification." This 10-hour course is designed for radar engineers, researchers, and AI enthusiasts who want to stay at the forefront of this rapidly evolving field.