Conference Information
RadarConf 2020: IEEE Radar Conference
https://www.radarconf20.org/Submission Date: |
2020-04-27 Extended |
Notification Date: |
2020-07-10 |
Conference Date: |
2020-09-21 |
Location: |
Florence, Italy |
Years: |
4 |
Viewed: 26020 Tracked: 8 Attend: 7
Call For Papers
Significant hardware advances combined with the availability of more and more sophisticated processing architectures, waveforms, and algorithms are pushing the development of innovative radars capable of satisfying the recent operational requirements in both civilian and defense domains. The international community will gather in Florence on September 21-25, 2020 to discuss and share the most recent findings in the many fields of application of radar techniques. Please, join us in this amazing city where Renaissance paved the way to the birth of modern science and to a new consideration of the role of mankind altogether. The IEEE Radar Conference is the World’s most comprehensive technical conference focused on radar techniques and applications. The 2020 edition of this conference will feature World-class speakers, tutorials, exhibits, and demo sessions.
Topics include, but are not limited to:
Antenna Technology
Radar Subsystems and Components
Radar Phenomenology and Scattering
Radar Systems
Space-Based Radar Systems and Missions
Space-Based/Airborne Radar Remote Sensing Applications
Ground-Based Remote Sensing Applications
Civilian Radar Applications
Radar and Multisensor Tracking
High-Resolution Radar Imaging and Classification
Cognitive Sensing and Learning Methods
Waveform Diversity and Design
Multistatic and Passive Radar
Radar Detection Theory
ECCM and Interference Cancellation
Radar and Sonar Signal/Data Processing
Last updated by Dou Sun in 2020-04-25
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| Full Name | Impact Factor | Publisher |
|---|---|---|
| Standards | MDPI | |
| IEEE Transactions on Radar Systems | IEEE | |
| Nano Today | 10.9 | Elsevier |
| IET Radar, Sonar & Navigation | 1.400 | IET |
| Diamond and Related Materials | 4.300 | Elsevier |
| Nanomaterials | 4.400 | MDPI |
| Calphad | 1.900 | Elsevier |
| IETE Journal of Research | 1.300 | Taylor & Francis |
| Robot Learning | ELSP | |
| IEEE Transactions on Big Data | 5.7 | IEEE |