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.
ASJC Scopus subject areas
- Electrical and Electronic Engineering