Statistical Approach for Automotive Radar Self-Diagnostics

Conference paper (2019)

Authors

N. Petrov Microwave Sensing, Signals & Systems - EEMCS
O.A. Krasnov Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2019 N. Petrov, O.A. Krasnov, Alexander Yarovoy
Calibration Automotive Radar Quality of Service Self-diagnostics
More Info
expand_more

Published Date

2019-10

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

In this paper, the problem of on-the-fly estimation of the
radar state (self-diagnostics) is considered. We propose to use repetitive
objects of the road infrastructure, such as lampposts, for continuous
diagnostics of the radar state. The selected approach allows accounting for the
external factors, such as water layer or dirt on the bumper, which can
significantly affect radar performance, but cannot be retrieved with the
internal calibration. The statistical model for RCS of repetitive targets is considered,
and the estimator of the actual radar gain from the received data is derived.
It is demonstrated that observing a few tens of targets is sufficient to
provide a reasonable estimation of the radar performance within the operational
mode.