High Fidelity Physics Simulation of 128 Channel MIMO Sensor for 77GHz Automotive Radar

Chipengo, Ushemadzoro; Sligar, Arien P.; Carpenter, Shawn

doi:10.1109/access.2020.3021362

Cited by 24 publications

(19 citation statements)

References 27 publications

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“…A linear array design is of practical interest as a building block (sub-array) of the 2-D array antenna. Furthermore, mmwave 1-D beam-steering arrays have found their application for the indoor communication [23] and automotive radar [24]. In these cases, the proposed open-ended RGW element concept can be readily utilized.…”

Section: B 1-d Array Rgw Elementmentioning

confidence: 99%

Wideband Open-Ended Ridge Gap Waveguide Antenna Elements for 1-D and 2-D Wide-Angle Scanning Phased Arrays at 100 GHz

Zhang

Vilenskiy

Ivashina

2022

Antennas Wirel. Propag. Lett.

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A new antenna element type based on the openended ridge gap waveguide (RGW) is proposed for phased array applications. This element type is of a particular interest at high mm-wave frequencies (≥ 100 GHz) owing to a contactless design alleviating active beam-steering electronics integration. The key challenge addressed here is a realization of a wide fractional bandwidth and scan range with high radiation efficiency. We demonstrate a relatively simple wideband impedance matching network comprised of an aperture stepped ridge segment and a single-pin RGW section. Furthermore, the E-and H-plane grooves are added that effectively suppress antenna elements mutual coupling. Results demonstrate a wide-angle beam steering (≥ 50 • ) over ≥ 20% fractional bandwidth at W-band with ≥ 89% radiation efficiency that significantly outperforms existing solutions at these frequencies. An experimental prototype of a 1×19 W-band array validates the proposed design concept through the embedded element pattern measurements.

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Section: B 1-d Array Rgw Elementmentioning

confidence: 99%

Wideband Open-Ended Ridge Gap Waveguide Antenna Elements for 1-D and 2-D Wide-Angle Scanning Phased Arrays at 100 GHz

Zhang

Vilenskiy

Ivashina

2022

Antennas Wirel. Propag. Lett.

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“…The first dimension is along the frequency samples in each chirp while the second dimension spans samples of a single frequency from chirp to chirp. A single range-Doppler map can be obtained by conducting a 2-dimensional fast Fourier transform (FFT) on this matrix [62]. However, the 2D FFT analysis can be reduced to a 1D FFT analysis since spectrograms only require the frequency content of the signal per CPI [20].…”

Section: B Simulation Workflow and Post Processing Validationmentioning

confidence: 99%

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

et al. 2021

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Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-Doppler signatures associated with VRUs. Specifically, machine learning (ML) algorithms can be trained to classify VRUs using the spectral content of radar signals. The performance of these models depends on the quality and quantity of the data used during the training process. Currently, data collection is typically done through measurements or low fidelity physics, primitive-based simulations. The feasibility of carrying out measurements to collect training data is typically limited by the vast amounts of data required and practicality issues when using VRUs like animals. In this paper, we present a computationally efficient, high fidelity physics-based simulation workflow that can be used to obtain a large quantity of spectrograms from the micro-Doppler signatures of VRUs. The simulations are conducted on full-scale VRU models with a 77 GHz, frequency-modulated continuouswave (FMCW) radar sensor model. Here, we collect the spectrograms of 4 targets; car, pedestrian, cyclist and dog at different speeds and angles-of-arrival. This data is then used to train a 5-layer convolutional neural network (CNN) that achieves nearly 100% classification accuracy after 5 epochs. Studies are conducted to investigate the impact of training data size, velocity and observation time window size on the accuracy of the CNN. Results from this study demonstrate how an accuracy of 95% can be realized using spectrograms obtained over a 0.2 s time window.

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“…First generation implementations of multiple-input multiple-output (MIMO) arrays [15], [16], [17] in automotive radar determined only the azimuth angle-of-arrival of targets in a traffic scene to provide the so-called bird's eye view [18], [19], [20]. In addition to range, velocity and azimuth angleof-arrival (AoA), radar sensors will need to also determine the height of detected targets through elevation AoA.…”

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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High Fidelity Physics Simulation of 128 Channel MIMO Sensor for 77GHz Automotive Radar

Cited by 24 publications

References 27 publications

Wideband Open-Ended Ridge Gap Waveguide Antenna Elements for 1-D and 2-D Wide-Angle Scanning Phased Arrays at 100 GHz

Wideband Open-Ended Ridge Gap Waveguide Antenna Elements for 1-D and 2-D Wide-Angle Scanning Phased Arrays at 100 GHz

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

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

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