High-resolution Automotive Radar Point Cloud Imaging and Processing

Jiang, Mengjie; Xu, Gang; Pei, Hao; Zhang, Hui; Guo, Kunpeng

doi:10.1109/piers55526.2022.9792662

Cited by 6 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By adding, subtracting or shifting the location and spacing of the elements, a wide range of possible MIMO arrays can be synthesized and tested in simulation. Since the transmitters emit identical FMCWs as described by Table 1, orthogonality of T x waveforms was achieved using a time domain multiplexing scheme (TDM) [20], [28].…”

Section: B Simulation Setupmentioning

confidence: 99%

“…), 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%

“…In order to realize affordable 4D radar sensors, cascaded frequency-modulated, continuous-waveform (FMCW) radars have been employed. 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%

See 2 more Smart Citations

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

Chipengo

2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: B Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chipengo

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For example, efforts have been made to improve the dynamic range of radar target detection, and to make the moving detection target stable [3]. Additionally, improvements have been conducted in the angular resolution of radar to capture more abundant target information [4][5][6]. More complex waveform designs have been employed to enhance the anti-interference ability [7,8].…”

Section: Introductionmentioning

confidence: 99%

Joint Calibration of MIMO Radar Arrays under Multiple Signal Sources

Wang,

Xie,

Yin

2023

IJAMM

View full text Add to dashboard Cite

Article Joint Calibration of MIMO Radar Arrays under Multiple Signal Sources Bin Wang 1, Xiaoyu Xie 1, and Shufei Yin 1,2,* 1 Shanghai Baolong Automotive Corporation 2 School of Automotive Engineering, Wuhan University of Technology * Correspondence: yinshufei@chinabaolong.net Received: 7 October 2023 Accepted: 2 November 2023 Published: 6 December 2023 Abstract: The 4D vehicle-mounted millimeter-wave radar can simultaneously measure the horizontal and vertical angles, which depends on the arrangement of the multiple-input multiple-output (MIMO) array antenna. In practical engineering applications, to ensure the calculation accuracy of horizontal angle and vertical angle, it is necessary to calibrate the array phase error and array antenna spacing error. In this paper, aiming at the simultaneous occurrence of array amplitude-phase errors and array antenna spacing errors, a mathematical model of MIMO antenna array errors is established. At the same time, the impact of array errors is analyzed by simulation. An array joint calibration method under multi-source is proposed, and the proposed calibration method is simulated and analyzed. The results show that the array phase error and the array spacing error can be calibrated at the same time through this calibration method, which can meet the calibration requirements in the mass production process of vehicle radar. The calibration method mentioned in this paper has the advantages of simplicity and high efficiency for the calibration of vehicle radar at this stage.

show abstract

“…M Illimeter-wave (mmWave) imaging has enormous potential in various applications, including security checks [1], human sensing [2][3][4], indoor mapping [5,6], and autonomous driving [7][8][9]. Its advantages of being penetrative, light-robust, and non-ionizing radiated, make it a highly promising imaging modality compared to optical cameras and X-rays [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

DI-Gesture: Domain-Independent and Real-Time Gesture Recognition with Millimeter-Wave Signals

Zhang

Chen

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

View full text Add to dashboard Cite

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

show abstract

High-resolution Automotive Radar Point Cloud Imaging and Processing

Cited by 6 publications

References 11 publications

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

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

Joint Calibration of MIMO Radar Arrays under Multiple Signal Sources

DI-Gesture: Domain-Independent and Real-Time Gesture Recognition with Millimeter-Wave Signals

Contact Info

Product

Resources

About