Statistical Approach for Automotive Radar Self-Diagnostics

Nikita Petrov; Oleg Krasnov; Alexander Yarovoy

Statistical Approach for Automotive Radar Self-Diagnostics

Nikita Petrov, Oleg Krasnov, Alexander Yarovoy

Microwave Sensing, Signals & Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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.

Original language	English
Title of host publication	2019 16th European Radar Conference (EuRAD)
Publisher	IEEE
Pages	117-120
Number of pages	4
ISBN (Electronic)	978-2-87487-057-6
ISBN (Print)	978-1-7281-3733-9
Publication status	Published - Oct 2019
Event	16th European Radar Conference: in the framework of the European Microwave Week 2019 - Paris, France Duration: 1 Oct 2019 → 4 Oct 2019 https://www.eumweek.com/conferences/eurad.html

Conference

Conference	16th European Radar Conference
Abbreviated title	EuRAD 2019
Country	France
City	Paris
Period	1/10/19 → 4/10/19
Internet address	https://www.eumweek.com/conferences/eurad.html

Keywords

Self-diagnostics
Quality of Service
Automotive Radar
Calibration

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https://ieeexplore.ieee.org/document/8904502

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Cite this

@inproceedings{615a112364da4adc9d0430b88a0c2e2c,

title = "Statistical Approach for Automotive Radar Self-Diagnostics",

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.",

keywords = "Self-diagnostics, Quality of Service, Automotive Radar, Calibration",

author = "Nikita Petrov and Oleg Krasnov and Alexander Yarovoy",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 16th European Radar Conference : in the framework of the European Microwave Week 2019, EuRAD 2019 ; Conference date: 01-10-2019 Through 04-10-2019",

year = "2019",

month = oct,

language = "English",

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pages = "117--120",

booktitle = "2019 16th European Radar Conference (EuRAD)",

publisher = "IEEE ",

address = "United States",

url = "https://www.eumweek.com/conferences/eurad.html",

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TY - GEN

T1 - Statistical Approach for Automotive Radar Self-Diagnostics

AU - Petrov, Nikita

AU - Krasnov, Oleg

AU - Yarovoy, Alexander

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

KW - Self-diagnostics

KW - Quality of Service

KW - Automotive Radar

KW - Calibration

M3 - Conference contribution

SN - 978-1-7281-3733-9

SP - 117

EP - 120

BT - 2019 16th European Radar Conference (EuRAD)

PB - IEEE

T2 - 16th European Radar Conference

Y2 - 1 October 2019 through 4 October 2019

ER -