@inproceedings{dbdbdd57495e43f794f7a82313e3d165,
title = "IDENTIFIABILITY STUDY OF NEAR-FIELD AUTOMOTIVE SAR",
abstract = "Automotive radar is the main sensor enabling autonomous driving and active safety features. It is required to provide high-resolution information on the vehicle's surroundings, accurately localize surrounding objects, and estimate their velocity in two dimensions. Conventional automotive radars operating in the far-field regime estimate only the target's radial velocity and cannot obtain its tangential velocity. However, the near-field propagation conditions allow the tangential radar target velocity estimation. This work proposes to extend the radar aperture using the synthetic aperture radar (SAR) approach for automotive applications to extend the near-field operation conditions to cover the automotive radar ranges of interest. This work derives the near-field synthetic aperture model and defines the near-field synthetic aperture to conduct an identifiability study using the Cram{\'e}r-Rao bound for the near-field model. It is demonstrated that it is possible to estimate the tangential radar target velocity in practical automotive scenarios.",
keywords = "Cram{\'e}r-Rao bound, SAR, Tangential velocity estimation, automotive radar, near-field",
author = "Michael Shifrin and Joseph Tabrikian and Igal Bilik",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
month = jan,
day = "1",
doi = "https://doi.org/10.1109/ICASSP48485.2024.10446433",
language = "American English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "8826--8830",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
}