A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

Fuchs, Jonas; Gardill, Markus; Lübke, Maximilian; Dubey, Anand Kumar; Lurz, Fabian

doi:10.1109/access.2022.3141587

Cited by 32 publications

(24 citation statements)

References 56 publications

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“…One option is to use neural networks to replace CFAR [156] or DOA estimation [76,157]. Readers can refer to [158] for a detailed survey of learning-based DOA estimation. Alternatively, there are also some efforts to perform end-to-end detection through neural networks.…”

Section: Pre-cfar Detectormentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

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Section: Pre-cfar Detectormentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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“…Finally, ML and artificial neural networks will boost sensing performance, where traditional modelbased approaches are reaching their limits. The applications of these sensing systems will range from high-resolution direction-of-arrival estimation [133] to object and environment classification [134] and complete scene understanding [135].…”

Section: Tc-23 Wireless Communicationsmentioning

confidence: 99%

The MTT-S Technical Coordinating and Future Directions Committee: Promoting Our Technical Communities-2022

Palazzi¹,

Siegel²,

Caverly³

2022

IEEE Microwave

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“…Recently, deep neural network (DNN) methods have also been applied for angle estimation [14]- [15]. These approaches require a significant overhead of producing a large amount of annotated data for training the DNN, and may be non-robust for changes in the sensor and the environment.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Automotive SAR With Radar Based Motion Estimation

Bialer¹,

Tirer²

2022

Preprint

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Automotive synthetic aperture radar (SAR) can achieve a significant angular resolution enhancement for detecting static objects, which is essential for automated driving. Obtaining high resolution SAR images requires precise ego vehicle velocity estimation. A small velocity estimation error can result in a focused SAR image with objects at offset angles. In this paper, we consider an automotive SAR system that produces SAR images of static objects based on ego vehicle velocity estimation from the radar return signal without the overhead in complexity and cost of using an auxiliary global navigation satellite system (GNSS) and inertial measurement unit (IMU). We derive a novel analytical approximation for the automotive SAR angle estimation error variance when the velocity is estimated by the radar. The developed analytical analysis closely predicts the true SAR angle estimation variance, and also provides insights on the effects of the radar parameters and the environment condition on the automotive SAR angle estimation error. We evaluate via the analytical analysis and simulation tests the radar settings and environment condition in which the automotive SAR attains a significant performance gain over the angular resolution of the short aperture physical antenna array. We show that, perhaps surprisingly, when the velocity is estimated by the radar the performance advantage of automotive SAR is realized only in limited conditions. Hence since its implementation comes with an increase in computation and system complexity as well as an increase in the detection delay it should be used carefully and selectively.

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A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

Cited by 32 publications

References 56 publications

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

The MTT-S Technical Coordinating and Future Directions Committee: Promoting Our Technical Communities-2022

Performance Analysis of Automotive SAR With Radar Based Motion Estimation

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