C-UAS Hub’s Post

Deep Learning for RF-based Drone Detection and Identification using Welch’s Method is a work by Mahmoud Almasri. https://hubs.la/Q02GmYqf0

A counter-drone visualisation platform incorporating sensor-data fusion is a European Commission Joint Research Centre (JRC) technical report by S. Lagaras, D. Amendola, and D. Anderson. This technical report details the development of a counter-unmanned aerial system (C-UAS) platform for sensor data fusion and visualization. It outlines the design, implementation, and integration of sensor inputs to effectively identify and track cooperative and non-cooperative drones in the airspace. https://hubs.la/Q02GmWRb0

FAA Publishes Updated FRIA Map and Information The Federal Aviation Administration (FAA) has released an updated FAA-Recognized Identification Area (FRIA) map and information for drone pilots. A FRIA is a designated geographic area where drones can be flown without the need for Remote ID equipment. https://hubs.la/Q02F_fnv0

MARSS Group Showcasing NiDAR Layered Defense at Red Sands 24.2 Companies worldwide will converge on Fort Drum, New York, from July 26th to August 8th for a full dress rehearsal ahead of the third edition of Red Sands 24.2, scheduled for September. https://hubs.la/Q02F_7D80

Airspace at Yuma Proving Ground Busy with JCO C-UAS Demo Their most recent event, the fifth at this location, spanned four weeks in June and was the most ambitious yet, concentrating on systems capable of detecting and defeating sUAS swarms. Out of nearly 60 proposals, 12 systems were selected by the JCO for demonstration, with nine participating in the event https://hubs.la/Q02FNjY00

Aaronia AG's AARTOS Supports Austrian Grand Prix Security https://hubs.la/Q02FyCnM0

What is the difference between passive and active drone detection? https://hubs.la/Q02FyCcv0

The rapid expansion of the drone industry has led to a significant increase in the number of low-altitude drones, raising concerns about collision avoidance and countermeasure strategies among these unmanned aerial vehicles. These challenges highlight the urgent need for effective air-to-air drone target detection. An ideal detection model must offer high accuracy, real-time capabilities, and a lightweight network architecture to balance precision and speed on embedded devices. To meet these requirements, the authors curated a dataset of over 10,000 images of low-altitude operating drones. This dataset includes diverse and intricate backgrounds, significantly enhancing the model’s training capacity. The authors applied a series of enhancements to the YOLOv5 algorithm to achieve lightweight object detection. https://hubs.la/Q02Fr2R20

Understanding Cope Cages: From Origins to Standardisation is a report by Julien Potin for the European Army Interoperability Centre. https://hubs.la/Q02Fr3xS0