Fixed and rotary wing mini / micro drones, or drone systems, have become an important threat to our security forces in recent years, especially as they can be easily supplied and used by terrorist organizations. Although various drone jammer systems have been found as a precaution against drone threats, the drone must be detected first in order to disable drone systems. Visual detection of drones can be difficult due to the fact that the dimensions of the threat are very small and sometimes seems are not possible due to weather conditions. On the other hand, due to the very short visibility distance of the drones, it is too late to prevent the danger. For this reason, radar systems stand out as the most critical system for detection of mini/micro UAVs in long range.
Azimuth machine scan+Euler frequency scan
Basic detection performance
Distance / Azimuth angle / Euler angles / Speed
Mini UAV (RCS=0.01m²)
Detection Resolving power
Azimuth Resolving power
Euler Resolving power
TWS / continuous track
Number of track targets
No less than 200
The system DR02 , powered by low RF output power in the Ku frequency band, has high-tech solid-state radio frequency design and digital-based radar architecture. Using a customized pulse-compression pulse doppler waveform, the system offers efficient modes of use that can be selected with different wave shapes and different angular rotational speeds by the help of digital radar architecture. The Retinar FAR-AD is not affected by the instant maneuvers of threats thanks to its high rotation speed of 30 rpm and monitors threats successfully.
The 3km drone radar detector DR02 can detect and track threats with its 40° elevation angle up to 7 km for land targets and mini/micro UAV’s up to 3 km. The Radar gives information on direction, speed, distance (up to 9 km) and orientation of targets through “User Interface Software” to its operator. The system, which can automatically classify the tracking information in tracking mode while scanning, increases classification performance with detailed algorithms in the Target Analysis Mode (Spectrogram Analysis) to resolve resulting uncertainties and improve classification reliability.