FMCW Radar Height Estimation of Moving Vehicles by Analyzing Multipath Reflections

Target classification is an important task of automotive radar systems. In this work, a concept for estimating the height of vehicles to allow for a differentiation between passenger cars, trucks, and others, is presented and discussed. Fixed installed radar sensors for traffic monitoring in the 77 GHz band are used to track and analyze radar echoes from individual vehicles as they move relative to the radar. Considering multipath propagation, which includes the ground reflection, the height of individual radar targets is estimated by analyzing the periodicity of the resulting amplitude modulation (AM) of the echo signal as a function of the horizontal distance from the radar. Two approaches have been realized to integrate the concept into an automotive FMCW signal processing scheme. Measurements in a test field using a trihedral corner reflector as an idealized target in heights from 0 to 2.5 m suggest, that the concept is suitable for height estimation in an automotive context.

On the Differences between L2-Boosting and the Lasso

We prove that L2-Boosting lacks a theoretical property which is central to the behaviour of l1-penalized methods such as basis pursuit and the Lasso: Whereas l1-penalized methods are guaranteed to recover the sparse parameter vector in a high-dimensional linear model under an appropriate restricted nullspace property, L2-Boosting is not guaranteed to do so. Hence, L2-Boosting behaves quite differently from l1-penalized methods when it comes to parameter recovery/estimation in high-dimensional linear models.