Introduction to Radar Using Python and MATLAB(R) Second Edition: With Applications in Artificial Intelligence and Machine Learning

Introduction to Radar Using Python and MATLAB(R), Second Edition: With Applications in Artificial Intelligence and Machine Learning provides a comprehensive, hands-on framework for understanding, designing, analyzing, and implementing modern radar systems. From electromagnetic field theory through advanced applications in synthetic aperture radar (SAR), this authoritative resource equips professionals with the integrated knowledge and practical tools required for next-generation development. The second edition extends coverage to cutting-edge technologies, integrating artificial intelligence and machine learning (AI/ML), alongside photonic receivers for enhanced radar dectection, target classification, tracking, SAR, and electronic warfare. An accompanying radar software tool suite directly supports practical exploration of these advanced concepts. Designed for interactive analysis, the software enables engineers to visualize radar performance, evaluate design trade-offs, and interactively test parameters across every chapter. The suite delivers interactive graphical user interfaces in Python, complemented by Jupyter notebooks, MATLAB applications, and MATLAB Live Scripts. These utilities update dynamic plots as parameters adjust, driving rapid experimentation and reinforcing the connection between theoretical concepts and system-level behavior.

This essential resource serves radar systems engineers, signal processing specialists, defense and aerospace professionals, and graduate students pursuing advanced radar design. Packed with over 1,000 equations, 300 detailed figures, 100 runnable code examples, and 200 comprehensive end-of-chapter problems for active learning, the text delivers the technical depth required to design, analyze, and optimize operational radar platforms. Engineers will gain an immediate competitive advantage by mastering both classical processing and modern AI-driven techniques to solve complex challenges in clutter rejection, target identification, and system integration.