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Radar AI - Deep Neural Networks for Enhanced Radar Point Cloud Generation

Radar AI - Deep Neural Networks for Enhanced Radar Point Cloud Generation

About This Training

Automotive Radar is a key sensor modality to enable higher levels of autonomous driving capabilities. The state-of-the-art algorithms for direction of arrival (DoA) estimation uses analytical signal processing methods like iterative sparse code analysis and other compressive sensing techniques as well as sparse array designs to achieve higher angular resolution.

These algorithms usually have a high computational and memory cost which makes it challenging to implement them on cost sensitive automotive embedded devices. In this training, we discuss a method which combines both analytical radar signal processing and modern deep learning techniques to create a hybrid high-resolution DoA estimation model with a low computational cost and memory footprint.

What You Will Learn

How performance radars with algorithm updates can be build
A method which combines both analytical radar signal processing and modern deep learning techniques to create a hybrid high-resolution direction of arrival (DoA) estimation model
Introduction to a highly efficient target classification neural network model for radar point clouds

Presented by

David Bethge
Product Marketing Manager Radar Systems, NXP Semiconductors
Ryan Wu
NXP Fellow, Technical Director Advanced Radar Solutions, NXP Semiconductors
Satish Ravindran
Senior Principal Engineer, Machine Learning, NXP Semiconductors

DURATION

55 min

LANGUAGE

English

SKILL LEVEL

Intermediate

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Additional Resources

Driving Automation and Radar Training Academy

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