Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping

Gottinger, Michael; Hoffmann, Marcel; Christmann, Mark; Schütz, Martin; Kirsch, Fabian; Gulden, Peter; Vossiek, Martin

doi:10.1109/jmw.2020.3034475

Cited by 70 publications

(27 citation statements)

References 53 publications

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“…We can classify radar sensors into low resolution (LR) and high resolution (HR). There are different technical routes to achieve high resolution, such as polarimetric radar [29], cooperative radars [30], multi-chip cascaded MIMO radar [13], synthetic aperture radar (SAR) [31], and spinning radar [32]. Most off-the-shelf radars can output a point cloud with range, azimuth angle, Doppler velocity, and RCS.…”

Section: Radar Datasetsmentioning

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: Radar Datasetsmentioning

confidence: 99%

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

Zhou

Liu

Zhao

et al. 2022

Sensors

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“…The advantages of radar sensors stimulate the ever increasing use of radar sensors in the automotive sector [2]. In order to generate high-resolution environmental maps, new high-performance sensor architectures can be created or more information can be extracted from existing radar systems through effective signal processing [3]. The best known approach for mapping the environment with help of radar data are grid maps (GM), which can be classified into target list-based occupancy grid maps (OGM) and raw data-based synthetic aperture radar (SAR) maps.…”

Section: Introductionmentioning

confidence: 99%

Radar-Based Mapping of the Environment: Occupancy Grid-Map Versus SAR

Grebner¹,

Schoeder²,

Janoudi³

et al. 2022

IEEE Microw. Wireless Compon. Lett.

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“…This allows the evaluation of bistatic radar responses with the same detection performance as for monostatic radar responses and to perform a DoA estimation with all monostatic and bistatic radar responses. The requirements of the signal processing can be relaxed if a common oscillator is distributed to all signal synthesizers [13], [21], [22]. However, the bistatic radar responses are still affected by uncorrelated phase noise.…”

Section: Introductionmentioning

confidence: 99%

Coherent Measurements of a Multistatic MIMO Radar Network With Phase Noise Optimized Non-Coherent Signal Synthesis

Dürr¹,

Bohm²,

Schwarz³

et al. 2022

IEEE J. Microw.

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For multistatic radar networks in the upper mm-wave range with a large spacing between its radar sensor nodes, a coherent signal distribution is very complex and thus very costly. Hence, it is desirable to generate the mm-wave signals individually for each radar sensor node, i.e., non-coherently. However, multistatic radar networks using a non-coherent signal distribution for its radar sensor nodes are affected by systematic errors and uncorrelated phase noise, which reduces the resolution and the detection performance of these systems. In this article, a novel non-coherent signal synthesis concept based on the direct digital synthesis (DDS) principle is presented for multistatic radar networks. Compared to a signal synthesis using a phase-locked loop (PLL), it is shown that the different phase noise behavior of the DDS is beneficial for bistatic signal paths between the radar sensor nodes. The presented hardware concept is considered and analyzed for three different types of coherency regarding the signal distribution: coherent, quasi-coherent, and incoherent. Measurements with a multiple-input multiple-output (MIMO) radar at 150 GHz prove that despite a non-coherent signal distribution, it is possible to achieve the same detection and imaging performance as with a fully coherent radar by using a DDS.

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Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping

Cited by 70 publications

References 53 publications

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

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

Radar-Based Mapping of the Environment: Occupancy Grid-Map Versus SAR

Coherent Measurements of a Multistatic MIMO Radar Network With Phase Noise Optimized Non-Coherent Signal Synthesis

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