A New Antenna Array and Signal Processing Concept for an Automotive 4D Radar

Stolz, Martin; Wolf, Maximilian; Meinl, Frank; Kunert, Martin; Menzel, Wolfgang

doi:10.23919/eurad.2018.8546603

Cited by 23 publications

(17 citation statements)

References 12 publications

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“…Moreover, the angular resolution achieved with all of them is still proportional to the number of virtual antennas of the system [22]. Therefore, a direct way of improving angular resolution would be to increase the number of transmitters and receivers of the system, which is the trend the automotive radar industry is following [23]- [25]. For example, the prototype presented in [25] has 4 transmitting elements and 16 receiving elements, or the Texas Instruments MIMO radar [26] used in this work with 12 transmitters and 16 receivers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a direct way of improving angular resolution would be to increase the number of transmitters and receivers of the system, which is the trend the automotive radar industry is following [23]- [25]. For example, the prototype presented in [25] has 4 transmitting elements and 16 receiving elements, or the Texas Instruments MIMO radar [26] used in this work with 12 transmitters and 16 receivers. However, this increase also raises the price and the size of the radar, both critical constraints in the automotive industry.…”

Section: Introductionmentioning

confidence: 99%

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Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

Roldan¹,

Fioranelli²,

Yarovoy³

2023

Preprint

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A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decisionmaking system of the vehicle.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

Roldan¹,

Fioranelli²,

Yarovoy³

2023

Preprint

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“…In all these systems, a computationally intensive postprocessing is required to resolve ambiguities, which is errorprone under poor conditions such as low signal-to-noise ratio (SNR) or in complex multi-target cases. For unambiguous, yet very sparse arrays as in [22], a complex reconstruction as shown in [8] is required to reduce the sidelobe level (SLL) in the DoA estimation, having the same limitations as above. To reduce the sparsity and thus effort in the post-processing, a higher channel count is required.…”

Section: Introductionmentioning

confidence: 99%

System Performance of a 79 GHz High-Resolution 4D Imaging MIMO Radar With 1728 Virtual Channels

Schwarz¹,

Riese²,

Dorsch³

et al. 2022

IEEE J. Microw.

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Future driver assistance and autonomous driving systems require high-resolution 4D imaging radars that provide detailed and robust information about the vehicle's surroundings, even in poor weather or lighting conditions. In this work, a novel high-resolution radar system with 1728 virtual channels is presented, exceeding the state-of-the-art channel count for automotive radar sensors by a factor of 9. To realize the system, a new mixed feedthrough and distribution network topology is employed for the distribution of the ramp oscillator signal. A multilayer printed circuit board is designed and fabricated with all components assembled on the back side, while the radio frequency signal distribution is on a buried layer and only the antennas are on the front side. The array is optimized to enable both multipleinput multiple-output operation and transmit beamforming. A sparse array with both transmit and receive antennas close to the transceivers is realized to form a 2D array with a large unambiguous region of 130 • × 75 • with a maximal sidelobe level of −15 dB. The array features a 3 dB beamwidth of 0.78 • × 3.6 • in azimuth and elevation, respectively. Radar measurements in an anechoic chamber show that even the individual peaks of the absorber in the chamber can be detected and separated in the range-angle cut of the 4D radar image. The performance is validated by measurements of a parking lot, where cars, a pedestrian, a fence, and a street lamp can be detected, separated, and estimated correctly in size and position.INDEX TERMS Advanced driver assistance systems (ADAS), automotive radar, chirp sequence modulation, direction-of-arrival (DoA) estimation, frequency modulated continuous wave (FMCW), imaging radar, local oscillator (LO) feedthrough, mm-wave, multiple-input multiple-output (MIMO), time delay correction.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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“…Furthermore, it lacks object height information and serves only as a last line of defense in most autonomous driving systems, acting as an advanced warning. With the advent of a new generation of 4D high-resolution imaging radars [ 5 , 6 , 7 ], promising applications have been seen. The imaging radar can produce LIDAR-like point-cloud data, contain rich Doppler information and it has all-weather characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Lastly, road edge height estimation, drain detection and parking lot detection were accomplished using this radar. Martin et al [ 7 ] used a new antenna array device that provides the ability to measure angles in azimuth and elevation. In order to estimate the direction of arrival, examples were executed by combining them.…”

Section: Introductionmentioning

confidence: 99%

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Bai

Zheng

et al. 2021

Sensors

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Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.

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A New Antenna Array and Signal Processing Concept for an Automotive 4D Radar

Cited by 23 publications

References 12 publications

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

System Performance of a 79 GHz High-Resolution 4D Imaging MIMO Radar With 1728 Virtual Channels

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

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