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Jack Richardson

Fixed and rotary wing mini / micro Unmanned Aerial Vehicles (UAVs) have become a threat to our security forces in recent years, especially as they can be easily supplied and used by terrorist organisations. Although various jammer systems are used as a precaution against UAV threats, they must be detected first in order to disable them. Visual detection can be difficult due to the fact that the dimensions of the threat are very small and sometimes not possible due to weather conditions. On the other hand, due to the very short visibility distance of UAVs, 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.

The Retinar FAR-AD Drone Detection Radar, developed by Meteksan Defense, is a radar system against mini/micro UAVs and threats from land. Completed within 8 months of the “Mini/Micro UAV Detection Radar System Contract”, signed with the Presidency of Defence Industries, Turkey in 2019, delivery of the Retinar FAR-AD Systems has been realised.

Tested

Powered by low RF output power in the Ku frequency band, the system has high-tech solid-state radio frequency design and digital-based radar architecture. Using a customised 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. It 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.