Detection and classification of unmanned aerial vehicles
AssigneeRobin Radar Facilities BV
InventorsSiete HAMMINGA, Bart PORTEGIJS, Hylke Jurjen Lijsbert WESTRA
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Abstract
A method and system for real-time and automated detection and classification of aerial objects, such as e.g. UAVs, on a radar plot level from digital radar images, wherein the digital radar images are created from radar return signals obtained by a conventional radar such as e.g. a continuous wave radar (e.g. FMCW radar), a phased-array radar, or a pulse radar, as opposed to using specialized micro-Doppler radars for classifying UAVs based on the Doppler effect created by the rotors or propellers of the UAVs. Further, the method and system allow for tracking the UAVs based on the digital radar images.
Claims18
A computer-implemented method for classifying an aerial object represented on a digital radar image, said aerial object being an aerial object that may pose a collision risk for air traffic, said method comprising:
- receiving at least one digital radar image originating from a radar system, said at least one digital radar image having a total area formed by individual pixels, said at least one digital radar image comprising a radar plot of said aerial object,
- selecting a sub-area of said total area, said sub-area comprising said radar plot of said aerial object, and
- subjecting said sub-area to a deep learning model for determining whether said radar plot represents an aerial object belonging to one class of aerial objects.
A computer system for classifying an aerial object represented on a digital radar image, said aerial object being an aerial object that may pose a collision risk for air traffic, said computer system comprising a deep learning model and a processor configured to:
- receive at least one digital radar image originating from a radar system, said at least one digital radar image having a total area formed by individual pixels, said at least one digital radar image comprising a radar plot of said aerial object,
- select a sub-area of said total area, said sub area comprising said radar plot of said aerial object,
- subject said sub-area to said deep learning model on said computer system to thereby determine whether said radar plot represents an aerial object belonging to one class of aerial objects.
Dependencies are inferred from the claim text.