US 12,078,752 B2
Detection and classification of unmanned aerial vehicles
Siete Hamminga, Heemstede (NL); Bart Portegijs, Leiden (NL); and Hylke Jurjen Lijsbert Westra, Leiden (NL)
Assigned to ROBIN RADAR FACILITIES BV, The Hague (NL)
Appl. No. 17/438,649
Filed by ROBIN RADAR FACILITIES BV, The Hague (NL)
PCT Filed Mar. 13, 2020, PCT No. PCT/EP2020/056921
§ 371(c)(1), (2) Date Sep. 13, 2021,
PCT Pub. No. WO2020/200704, PCT Pub. Date Oct. 8, 2020.
Claims priority of application No. 19166246 (EP), filed on Mar. 29, 2019.
Prior Publication US 2022/0189326 A1, Jun. 16, 2022
Int. Cl. G06V 10/82 (2022.01); B64C 39/02 (2023.01); G01S 7/41 (2006.01); G06V 10/764 (2022.01); G06V 20/17 (2022.01); G06V 20/54 (2022.01); G08G 5/04 (2006.01); B64U 101/30 (2023.01)
CPC G01S 7/417 (2013.01) [B64C 39/024 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/17 (2022.01); G06V 20/54 (2022.01); G08G 5/045 (2013.01); B64U 2101/30 (2023.01)] 17 Claims
OG exemplary drawing
 
1. 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 a chronological series of digital radar images originating from a radar system, said digital radar images each having a total area formed by individual pixels, each of said digital radar images comprising a radar plot of said aerial object,
combining radar plots of said aerial object into a sequence of radar plots of said aerial object,
obtaining from said sequence of radar plots of said aerial object a track for said aerial object,
for each of said digital radar images selecting a sub-area of said total area, said sub-area comprising said radar plot of said aerial object,
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, and
concluding that said track represents an aerial object belonging to said one class when it is determined for a plurality of said radar plots in said sequence that the radar plot subjected to said deep learning model belongs to said one class.