Monostatic RCS measurements of a passenger car mock-up at 77 GHz frequency in virtual environment

Buddappagari, Sreehari; Schwind, Andreas; Stephan, Ralf; Hein, Matthias

doi:10.23919/eumc.2019.8910719

Cited by 10 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 shows a radar range-Doppler map, also called a radar image for five targets T 1 , T 2 , T 3 , T 4 , T 5 within the ranges of 50 m, 51 m, 52 m, 53 m, 54 m and moving at the velocities of -5 m/s, 10 m/s, 15 m/s, 20 m/s, 21 m/s, respectively, when SNR = 5 dB. According to [55], the RCS of a car at 77 GHz in decibel square meters is about 20 dBsm, which is equivalent to 100 m 2 . Based on this value, we generated the five targets with RCSs equal to 100 m 2 , 350 m 2 , 120 m 2 , 150 m 2 , 50 m 2 , respectively.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

An Efficient OFDM-Based Monostatic Radar Design for Multitarget Detection

Delamou,

Noubir,

Dang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In this paper, we propose a monostatic radar design for multitarget detection based on orthogonal-frequency division multiplexing (OFDM), where the monostatic radar is co-located with the transmit antenna. The monostatic antenna has the perfect knowledge of the transmitted signal and listens to echoes coming from the reflection of fixed or moving targets. We estimate the target parameters, i.e., range and velocity, using a two-dimensional (2D) periodogram. By this setup we improve the periodogram estimation performance under the condition of low signal-to-noise ratio (SNR) using Zadoff-Chu precoding (ZCP) and the discrete Fourier transform channel estimation (DFT-CE). Furthermore, since the dimensions of the data matrix can be much higher than the number of targets to be detected, we investigate the sparse Fourier transform-based Fourier projection-slice (FPS-SFT) algorithm and compare it to the 2D periodogram. An appropriate system parameterization in the industrial, scientific, and medical (ISM) band of 77 GHz, allows to achieve a range resolution of 30.52 cm and a velocity resolution of 66.79 cm/s.INDEX TERMS Fourier slice theorem, joint communication and radar sensing (JCAS), monostatic radar, OFDM, Zadoff-Chu precoding.

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

An Efficient OFDM-Based Monostatic Radar Design for Multitarget Detection

Delamou,

Noubir,

Dang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For automotive radar, a number of researchers have carried out reflectivity measurements in anechoic chambers obtained with automotive-grade radars, or Vector Network Analyzers [7], [8]. Here, a vehicle has been placed on a rotating plate and its 360 °RCS profile has been obtained from the received signal strength for a given rotation angle.…”

Section: A Related Workmentioning

confidence: 99%

A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation

2023

View full text Add to dashboard Cite

In virtual validation of automated driving, trustworthy simulation models of perception sensors are required. Radar sensors are particularly hard to model, as their measurements are notoriously difficult to interpret. This is due to their complex measurement principle, involving multi-path propagation of mm-waves, varying backscattering characteristics of objects, and further factors such as limited measurement ranges and resolutions that introduce uncertainty to the measurements. This work presents a method for studying the backscatter characteristics of vehicles under real-world driving conditions. A slalom-like driving scenario, which is representative for road-driving where the vehicle is visible under different aspect angles, has been designed. It aims at a high level of reproducibility of the trajectories driven by the vehicles, hence reducing additional sources of uncertainty that were otherwise present in the measurements. In a large-scale measurement campaign, 13 vehicles have been studied. The vehicles-under-test are observed by multiple radars, mounted at different heights, and carry reference sensors for obtaining their positions. In this paper, we present the measurement campaign and show major findings from our measurement results. Our focus lies on drawing conclusions for trustworthy sensor simulation. Both sensor measurement data, and MATLAB code for data analysis are made publicly available alongside this paper.

show abstract

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Monostatic RCS measurements of a passenger car mock-up at 77 GHz frequency in virtual environment

Cited by 10 publications

References 5 publications

An Efficient OFDM-Based Monostatic Radar Design for Multitarget Detection

An Efficient OFDM-Based Monostatic Radar Design for Multitarget Detection

A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation

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

Contact Info

Product

Resources

About