@article{ace6dd700e794494b9a2ef4cd3e4ee27,
title = "Combining automotive radar and LiDAR for surface detection in adverse conditions",
abstract = "Automotive radar and light detection and ranging (LiDAR) sensors have complementary strengths and weaknesses for 3D surface mapping. We present a method using Markov chain Monte Carlo sampling to recover surface returns from full-wave longitudinal signals that takes advantage of the high spatial resolution of the LiDAR in range, azimuth and elevation together with the ability of the radar to penetrate obscuring media. The approach is demonstrated using both simulated and real data from an automotive system.",
author = "Wallace, {Andrew M.} and Saptarshi Mukherjee and Bemsibom Toh and Alireza Ahrabian",
note = "Funding Information: This work was supported by Jaguar Land Rover, the UK Engineering and Physical Research Council, EP/N012402/1 (TASCC: Pervasive low‐TeraHz and Video Sensing for Car Autonomy and Driver Assistance) and EP/S000631/1 (Signal Processing for the Information Age) and the MOD University Defence Research Collaboration (UDRC) in Signal Processing. Publisher Copyright: {\textcopyright} 2021 The Authors. IET Radar, Sonar & Navigation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = apr,
doi = "10.1049/rsn2.12042",
language = "English",
volume = "15",
pages = "359--369",
journal = "IET Radar, Sonar and Navigation",
issn = "1751-8784",
publisher = "Institution of Engineering and Technology",
number = "4",
}