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Statistical Approach for Automotive Radar Self-Diagnostics. / Petrov, Nikita; Krasnov, Oleg; Yarovoy, Alexander.

2019 16th European Radar Conference (EuRAD). IEEE, 2019. p. 117-120.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Petrov, N, Krasnov, O & Yarovoy, A 2019, Statistical Approach for Automotive Radar Self-Diagnostics. in 2019 16th European Radar Conference (EuRAD). IEEE, pp. 117-120, 16th European Radar Conference, Paris, France, 1/10/19.

APA

Petrov, N., Krasnov, O., & Yarovoy, A. (2019). Statistical Approach for Automotive Radar Self-Diagnostics. In 2019 16th European Radar Conference (EuRAD) (pp. 117-120). IEEE.

Vancouver

Petrov N, Krasnov O, Yarovoy A. Statistical Approach for Automotive Radar Self-Diagnostics. In 2019 16th European Radar Conference (EuRAD). IEEE. 2019. p. 117-120

Author

Petrov, Nikita ; Krasnov, Oleg ; Yarovoy, Alexander. / Statistical Approach for Automotive Radar Self-Diagnostics. 2019 16th European Radar Conference (EuRAD). IEEE, 2019. pp. 117-120

BibTeX

@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 ‘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.",
year = "2019",
month = "10",
language = "English",
isbn = "978-1-7281-3733-9",
pages = "117--120",
booktitle = "2019 16th European Radar Conference (EuRAD)",
publisher = "IEEE",
address = "United States",

}

RIS

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

ER -

ID: 57454724