Combining automotive radar and LiDAR for surface detection in adverse conditions | Zendy

Wallace Andrew M. | Zendy; Mukherjee Saptarshi | Zendy; Toh Bemsibom | Zendy; Ahrabian Alireza | Zendy

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Combining automotive radar and LiDAR for surface detection in adverse conditions

Author(s) -

Wallace Andrew M.,

Mukherjee Saptarshi,

Toh Bemsibom,

Ahrabian Alireza

Publication year - 2021

Publication title -

iet radar, sonar and navigation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.489

H-Index - 82

eISSN - 1751-8792

pISSN - 1751-8784

DOI - 10.1049/rsn2.12042

Subject(s) - lidar , radar , automotive industry , ranging , azimuth , remote sensing , radar imaging , sampling (signal processing) , range (aeronautics) , monte carlo method , elevation (ballistics) , computer science , adverse weather , environmental science , geology , computer vision , optics , aerospace engineering , meteorology , engineering , geography , physics , telecommunications , statistics , mathematics , filter (signal processing) , structural engineering

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.

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