A Sparse Array for 77 GHz 4D High-Resolution Imaging Radar Based on Entropy Model and Convex Optimization

Niu, Jia; Wu, Ya Fei; Cheng, Yu Jian; Mou, Lun Wei; Li, Ting Jun; Yang, Hai Ning; Pu, Shi Liang; Shen, Lin; Qian, Tong

doi:10.1109/iwem53379.2021.9790462

Cited by 3 publications

(1 citation statement)

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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%

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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