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

Schwarz, Dominik; Riese, Nico; Dorsch, Ines; Waldschmidt, Christian

doi:10.1109/jmw.2022.3196454

Cited by 28 publications

(11 citation statements)

References 33 publications

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“…With the advancement of multiple-input-multiple-output (MIMO) technology [43], 4-D mm-wave radar with additional elevation angle information has been suggested and is already being manufactured on a small scale [44]. It can generate denser point clouds than 3-D mm-wave radar, which is very important for perception tasks.…”

Section: B Mm-wave Radarsmentioning

confidence: 99%

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Liu

Shen

Tian

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Metaverses have caused significant changes in the industry and their academic foundation can be traced back to the term cyber-physical-social systems (CPSS), which was proposed in 2010. Radar is an important sensor in sensing systems that are widely applied in many fields, especially, in autonomous driving. To deal with the complex environment, smart radars with realtime information processing capabilities are required. Human factors play a critical role in the operation and management of radar systems, thus, digital twins' radars in cyber-physical systems (CPS) are unable to achieve intelligence in CPSS due to an incomplete consideration of human involvement. For this consideration, we propose a novel framework of RadarVerses for smart radars in metaverses based on ACP-based parallel intelligence, which is also known as cyber-physical-social intelligence (CPSI). RadarVerses consist of five main parts which are physical radars, descriptive radars, predictive radars, prescriptive radars, and deep radars. To construct RadarVerses at the technical level, we introduce four main technical foundations: 1) communication technology; 2) scenarios engineering; 3) foundation models; and 4) digital workers. In addition, we also provide a case study about LiDARs' predictive maintenance of accumulated snow in RadarVerses.

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Section: B Mm-wave Radarsmentioning

confidence: 99%

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Liu

Shen

Tian

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…The phase offsets in Fig. 6(a) are determined after compensating the bistatic influence of the target by means of the simulation model (9). The mean deviation is 1.1°for d Target = 92 mm and 1.0°for d Target = 192 mm.…”

Section: ) Accuracy Of Determined Calibration Parametersmentioning

confidence: 99%

“…These systems are employed for measurements of the direction-of-arrival (DoA) estimation of incoming waves or beamforming at the transmitter. Latest works focus on the DoA estimation in azimuth and elevation to achieve 4D imaging [7]- [9]. As these operations require a precise array model, calibration of the antenna array is crucial.…”

Section: Introductionmentioning

confidence: 99%

Highly Efficient Calibration of Antenna Arrays by Active Targets in the Near-Field

Linder¹,

Meinecke²,

Halici³

et al. 2023

IEEE Open J. Antennas Propag.

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To enhance the capabilities of high-resolution imaging or beamforming, the trend goes towards arrays with high-channel count and large aperture sizes. The operation at increasing mm-wave frequencies exacerbates the consequences of manufacturing tolerances on the array performance, further necessitating the development of high-quality calibrations. Thereby, a large number of array response vectors, addressed in the following as data points, is stored. Typically, one data point is measured at a time for each angle. To reduce the calibration effort, hereby defined as the number of required measurements, a calibration setup employing active calibration targets (ACT) is proposed. This setup allows the storage of a multitude of data points within a single measurement. In addition, this measurement setup is placed in the near-field of the array to relax the requirements on the size of the anechoic chamber. Employing multiple targets makes it challenging to compensate the near-field effects. Therefore, methods are discussed to ensure a consistent quality of the calibration considering the near-field influences from the ACTs. Exemplary measurements deploying an antenna array operating at 78.5 GHz demonstrate that this calibration measurement setup employing three ACTs placed in the near-field of the array reduces the calibration effort by a factor of 3 compared to state-of-the-art setups. The average deviation of the antenna phase relations amounts to 4.1 degree, proving that the presented setup achieves the same calibration quality as state-of-the-art setups.INDEX TERMS calibration, direction-of-arrival (DoA) estimation, imaging radar, MIMO radar, phased arrays, radar measurements.

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“…), maneuvered around or avoided by braking in the worst case as part of active-safety definition [21]. Recent efforts have addressed this need by introducing MIMO 4D radar sensors that can determine the range, velocity, azimuth and elevation AoA of a target and produce a point cloud from the traffic scene [7], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30]. A sensor-fusion strategy involving a 4D radar sensor and monovision camera was used in [22] to conduct 3D mapping of traffic scenes.…”

Section: Introductionmentioning

confidence: 99%

“…A 2-chip cascaded FMCW radar (6 T x , 8 R x ) was implemented in [26] while a 4-chip, cascaded FMCW radar (12 T x , 16 R x , 192 virtual channels) sensor was implemented in [28]. Recently, 12 monolithic microwave integrated circuits (MMICs) were used to create a 4D imaging radar with 1,728 virtual channels (36 T x , 48 R x ) [30].…”

Section: Introductionmentioning

confidence: 99%

High Fidelity Physics-Based Simulation of a 512-Channel 4D-Radar Sensor for Automotive Applications

Chipengo

2023

IEEE Access

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Radar has emerged as a core sensing technology for many active-safety and comfort related advanced driver assistance systems (ADAS) being deployed in today's vehicles. Using radar technology, the ego vehicle can simultaneously detect the range and velocity of multiple targets. With multiple-input, multiple-output (MIMO) arrays, it is also possible to detect the angle of arrival of targets in azimuth and elevation. 4D automotive radar sensors can determine the range, velocity, azimuth and elevation anglesof-arrival (AoA) of targets in a traffic scene. Currently, radar-returns from traffic scenes have been mainly obtained through measurement. While measurement is valuable, it can be costly, time consuming, restrictive due to practicality issues and also unsafe in some corner-case traffic scenarios. Simulation has emerged as a potential alternative source of synthetic radar-returns that can be used to develop, test and refine signal processing techniques and detection algorithms. A key challenge in simulation has been to retain an accurate representation of actors in full-scale traffic scenes while still being able to solve electrically large problems efficiently. The introduction of MIMO arrays further increases the complexity and computational load demands of such simulations. In this paper, we present a computationally efficient, high fidelity, physics-based simulation workflow for a 77 GHz frequency-modulated continuous-waveform (FMCW)based, 512-channel MIMO radar sensor. We demonstrate how the synthetic radar returns obtained from full-scale traffic scene simulations can be used to create 4D-radar point clouds. The accuracy of the synthetic radar returns is then evaluated by overlaying the resulting 4D-radar point clouds on 4 corresponding full-scale traffic scenes with varying levels of complexity. Results from this study demonstrate how accurate radar returns obtained from simulation can be used to develop next-generation radar sensors for autonomous vehicles.

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

Cited by 28 publications

References 33 publications

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS

Highly Efficient Calibration of Antenna Arrays by Active Targets in the Near-Field

High Fidelity Physics-Based Simulation of a 512-Channel 4D-Radar Sensor for Automotive Applications

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