Automotive Radar Signal Processing: Research Directions and Practical Challenges

Automotive radar is used in many applications of advanced driver assistance systems and is considered as one of the key technologies for highly automated driving. An overview of state-of-the-art signal processing in automotive radar is presented along with current research directions and practical c...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 15; no. 4; pp. 865 - 878
Main Authors: Engels, Florian, Heidenreich, Philipp, Wintermantel, Markus, Stacker, Lukas, Al Kadi, Muhammed, Zoubir, Abdelhak M.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Advanced driver assistance systems
Automotive electronics
Automotive engineering
Automotive radar
autonomous vehicles
Deep learning
Machine learning
MIMO
MIMO radar
Production
Radar applications
Radar signal processing
Signal processing
State-of-the-art reviews
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:Automotive radar is used in many applications of advanced driver assistance systems and is considered as one of the key technologies for highly automated driving. An overview of state-of-the-art signal processing in automotive radar is presented along with current research directions and practical challenges. We provide a comprehensive signal model for the multiple-target case using multiple-input multiple-output schemes, and discuss a practical processing chain to calculate the target list. To demonstrate the capabilities of a modern series production high-performance radar sensor, real data examples are given. An overview of conventional target processing and recent research activities in machine learning and deep learning approaches is presented. Additionally, recent methods for practically relevant radar-camera fusion are discussed.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2021.3063666