TY - GEN
T1 - Exploiting Virtual Array Diversity for Accurate Radar Detection
AU - Guan, Junfeng
AU - Madani, Sohrab
AU - Ahmed, Waleed
AU - Hussein, Samah
AU - Gupta, Saurabh
AU - Hassanieh, Haitham
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10× finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.
AB - Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10× finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.
UR - http://www.scopus.com/inward/record.url?scp=85177566015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85177566015&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10094572
DO - 10.1109/ICASSP49357.2023.10094572
M3 - Conference contribution
AN - SCOPUS:85177566015
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -