ICASSP 2020accepted0 citations
Slow-Time MIMO-FMCW Automotive Radar Detection with Imperfect Waveform Separation
Pu Wang, Petros Boufounos, Hassan Mansour, Philip V. Orlik
Abstract
This paper considers object detection in the case of imperfect waveform separation, in the context of automotive radars with a slow-time MIMO-FMCW signaling scheme. We develop an explicit signal model that accounts for waveform separation residuals and propose a Kronecker subspace-based object detector in the framework of generalized likelihood ratio test (GLRT). Our exact theoretical analysis under both hypotheses shows that the proposed detector holds the desired property of constant false alarm rate (CFAR). Numerical simulations validate our proposed object detection scheme.
BibTeX
@inproceedings{icassp2020_slowtimemimofmcw,
title = {Slow-Time MIMO-FMCW Automotive Radar Detection with Imperfect Waveform Separation},
author = {Pu Wang and Petros Boufounos and Hassan Mansour and Philip V. Orlik},
booktitle = {ICASSP 2020},
year = {2020}
}