Estimating the Magnitude and Phase of Automotive Radar Signals Under Multiple Interference Sources With Fully Convolutional Networks

Estimating the Magnitude and Phase of Automotive Radar Signals Under Multiple Interference Sources With Fully Convolutional Networks

Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles, generating corrupted range profiles and range-...

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Bibliographic Details
Main Authors: Nicolae-Catalin Ristea, Andrei Anghel, Radu Tudor Ionescu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Autonomous driving
automotive radar
interference mitigation
deep learning
phase estimation
fully convolutional networks
Online Access:https://ieeexplore.ieee.org/document/9615067/

Internet

https://ieeexplore.ieee.org/document/9615067/

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