Szczegóły publikacji

Opis bibliograficzny

Uncertainty propagation for vehicle detections in experimentally validated radar model for automotive application / Adam MARTOWICZ, Alberto GALLINA, Grzegorz KARPIEL // W: MMAR 2019 : 24th international conference on Methods and Models in Automation and Robotics : 26–29 August 2019, Międzyzdroje, Polska : abstracts. — Szczecin : ZAPOL Sobczyk, [2019]. — Dod. e-ISBN 978-1-7281-0933-6. — ISBN: 978-83-7518-922-3; e-ISBN: 978-1-7281-0932-9. — S. 72–73. — Pełny tekst w: MMAR 2019 [Dokument elektroniczny] : 24th international conference on Methods and Models in Automation & Robotics : August 26–29, 2019, Międzyzdroje, Poland / Faculty of Electrical Engineering. West Pomeranian University of Technology Szczecin. — [Piscataway] : IEEE, cop. 2019. — Dysk Flash. — S. 606–611. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 611, Abstr.

Autorzy (3)

Słowa kluczowe

radar modeluncertaintyvehicle detectionexperimental validationautomotive application

Dane bibliometryczne

ID BaDAP123700
Data dodania do BaDAP2019-09-05
DOI10.1109/MMAR.2019.8864641
Rok publikacji2019
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaInstitute of Electrical and Electronics Engineers (IEEE)
KonferencjaInternational Conference on Methods and Models in Automation and Robotics 2019

Abstract

The paper is devoted to the development and experimental validation of a radar model elaborated for an automotive application. The described numerical model takes into account the physical properties of a typical automotive radar, the environmental conditions as well as the properties of targeted objects. Moreover, a dedicated, very efficient algorithm of geometric transformations - implemented in the radar model for simulation of wave scattering phenomena - is presented. A phenomenological model of uncertainty is enclosed to the algorithm to represent more effectively radar detections. The solution presented by the authors is thus ready to be used in hardware-in-the-loop simulations of various road traffic scenarios. © 2019 IEEE.

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