Machine and Deep Learning for Drone Radar Recognition by Micro-Doppler and Kinematic criteria
| Autor: | Daniel A. Brooks, Frédéric Barbaresco, Claude Adnet |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
020301 aerospace & aeronautics
Computer science business.industry Deep learning 02 engineering and technology Kinematics Tracking (particle physics) 01 natural sciences Drone law.invention 010104 statistics & probability symbols.namesake 0203 mechanical engineering law Anticipation (artificial intelligence) symbols Computer vision Artificial intelligence 0101 mathematics Interception Radar business Doppler effect |
| Zdroj: | 2020 IEEE Radar Conference (RadarConf20). |
| DOI: | 10.1109/radarconf2043947.2020.9266371 |
| Popis: | Illegal, malicious or dangerous uses of drones, require developing systems capable of detecting, tracking and recognizing them in a non-collaborative way, and with enough anticipation in order to assign adapted interception means to the threat. The reduced size of autonomous aircraft makes it difficult to be detected over long distances with sufficient awareness based on conventional techniques, and seems more suitable for observation by radar sensors. However, the radiofrequency detection of this kind of object poses other difficulties to be solved due to their slow speed characteristics which can cause confusion with other mobile echoes like land vehicles, birds and vegetation movements agitated by atmospheric turbulence. It is therefore necessary to design robust classification methods for these echoes to ensure their discrimination relative to criteria characterizing their movements (micro-movements of their moving parts and kinematic movements of their main body). |
| Databáze: | OpenAIRE |
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