Suche nach „[Alexander] [Weinberger]“ hat 6 Publikationen gefunden

DigitalElektrotechnik und MedientechnikTC Freyung

Vortrag

Alexander Weinberger

Einsatz von Software Defined Radio zur Detektion und Analyse von Funksignalen ziviler Drohnen

U.T.SEC 2019 - Summit for Drones, Unmanned Technologies & Security, Nürnberg

2019

DigitalElektrotechnik und MedientechnikTC Freyung

Beitrag (Sammelband oder Tagungsband)

Stefan Kunze, Alexander Weinberger, Rainer Pöschl

A Software Defined Radio Based Implementation for the Radio Frequency Analysis of Signals from Unmanned Aerial Systems

Proceedings of the 29th International Conference Radioelektronika 2019 (April 16-18, 2019; Pardubice, Czech Republic)

2019

DigitalElektrotechnik und MedientechnikTC Freyung

Beitrag (Sammelband oder Tagungsband)

Stefan Kunze, M. Schiefer, Alexander Weinberger

A GNU Radio Imprementation for Frequency Hopping Spread Spectrum Receiver Synchronization

Proceedings of the 29th International Conference Radioelektronika 2019 (April 16-18, 2019; Pardubice, Czech Republic)

2019

DigitalElektrotechnik und MedientechnikTC Freyung

Vortrag

Alexander Weinberger

Einsatz von Software Defined Radio zur Detektion und Analyse von Funksignalden ziviler Drohnen

U.T.SEC 2019 - Summit for Drones, Unmanned Technologies & Security, Nürnberg

2019

DigitalElektrotechnik und MedientechnikTC Freyung

Vortrag

Stefan Kunze, Alexander Weinberger, Rainer Pöschl

Concept for a Software Defined Radio Based System for Detection, Classification and Analysis of Radio Signals from Civilian Unmanned Aerial Systems

the 2nd URSI Atlantic Radio Science Conference (URSI AT-RASC), Maspalomas, Gran Canaria, Spanien

2018

DigitalElektrotechnik und MedientechnikTC Freyung

Beitrag (Sammelband oder Tagungsband)

Stefan Kunze, Alexander Weinberger, Rainer Pöschl

Concept for a Software Defined Radio Based System for Detection, Classification and Analysis of Radio Signals from Civilian Unmanned Aerial Systems

Proceedings of the 2nd URSI Atlantic Radio Science Conference (URSI AT-RASC) [May 28th-June 1st, 2018; Maspalomas, Gran Canaria, Spain]

2018

ISBN: 978-1-5386-3764-7

DOI: 10.23919/URSI-AT-RASC.2018.8471398

Abstract anzeigen

The recent advances in the field of civilian Unmanned Aerial Systems have enabled a wide range of applications and facilitated their wide spread use. However, these systems can also pose a threat to the civilian population. As part of a research project to counter these threats, a Software Defined Radio based system for detection, classification and analysis of radio signals from civilan drones is proposed in this paper. A short look at related work, focusing on the application of machine learning techniques for the classification of radio signals is presented and a concept for the implementation of detection and classification system is proposed.