• views: 83
• date: 17 August 2026
• posted by: voska89

What you'll learn

⚡ Understand how phased antenna arrays and spatial phase differences enable direction-of-arrival estimation.
⚡ Implement and compare Spatial FFT, Bartlett, Capon/MVDR, MUSIC, and ESPRIT DoA estimation methods.
⚡ Evaluate angular resolution, estimation accuracy, and limitations of conventional and super-resolution DoA methods.
⚡ Process real FMCW radar measurements from a TDM MIMO virtual antenna array for direction-of-arrival estimation.
⚡ Validate DoA algorithms using real AWR2243 measurements with single-target and two-target radar scenarios.

Requirements

❗ Basic understanding of digital signal processing and complex-valued signals.
❗ Basic knowledge of Python and NumPy is recommended.
❗ Basic familiarity with FMCW radar is helpful but not required.
❗ No radar hardware is required; measurement data and processing scripts are provided.

Description

DoA Estimation for Automotive FMCW Radar introduces the theory, implementation, and practical validation of direction-of-arrival estimation methods used in automotive radar signal processing.
The course begins with phased-array fundamentals and explains how spatial phase differences across antenna elements encode target direction. From this foundation, the main DoA estimation methods are developed and compared: Spatial FFT, Bartlett beamforming, Capon/MVDR, MUSIC, and ESPRIT.
Each method is examined through numerical examples and simulations, with emphasis on its operating principle, angular resolution, assumptions, strengths, and practical limitations.
The theoretical part is followed by validation using real measurements acquired with a Texas Instruments AWR2243 automotive FMCW radar configured as a two-transmitter, four-receiver TDM MIMO system forming an eight-element virtual antenna array.
Students work with provided measurement data and Python processing scripts to examine single-target and two-target scenarios and compare the behavior of conventional and super-resolution DoA methods on real radar data.
By the end of the course, students will understand how the main DoA estimation algorithms work, how they differ in resolution and robustness, and how they can be applied to practical FMCW radar measurements.
The course is intended for radar engineers, DSP engineers, researchers, graduate students, and developers working with automotive radar, array signal processing, beamforming, and MIMO radar systems.

Who this course is for

⭐ Radar and signal-processing engineers who want to understand and implement practical DoA estimation algorithms.
⭐ Automotive radar engineers working with FMCW, antenna arrays, beamforming, or MIMO radar processing.
⭐ Researchers and graduate students studying array signal processing, beamforming, and super-resolution DoA methods.
⭐ Python developers and DSP engineers who want to validate DoA algorithms using real automotive radar measurements.

Homepage

https://www.udemy.com/course/doa-estimation-for-fmcw

   
   
   

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