SkyRadar Blog

Fast reaction and decision-making of the RADAR operator is a key factor in ATC. There is a need to develop techniques for the automatic extraction and fine-granulated classification of RADAR objects, allowing for faster and better decision making and more effective processes.

RADAR is short for RAdio Detection And Ranging. The principle of RADAR systems is not very difficult to understand. It is simply because most solid objects reflect radio waves, e.g., electromagnetic radiation with wavelengths superior to infrared. A RADAR therefore sends radio waves through an emitter and captures the “response”, e.g the reflecting signal through a receiver.

Deep Learning Algorithms produce better-than-human results in image recognition, generating a close to zero fault rate [1]. This article shows how this works in radar technology and explains, how Artificial Intelligence can be taught in University Education and NextGen ATC qualification.

Artificial Intelligence is starting to become a game changer in Aviation. In this article we look at its expected impacts on radar technology and describe SkyRadar's solutions to qualify ATCO and ATSEP on applied AI.

We started SkyRadar.com more than 10 years ago. We were driven by the idea of blending radar and Web technology. Our idea was to bring radars to unknown performance, all while bringing costs down to the bottom-line. Now we are adding medical applications, robotics, AI and industrial applications. And it is just the beginning...

Technology is always changing. A large percentage of it will make our lives easier by enhancing how we learn or go about our daily jobs in ways that were never thought before. Artificial intelligence and machine learning stand at the forefront of technology’s future, including their use in radar technology. The purpose of this article is to define what AI and machine learning are, how they relate to each other and what their role may be in radar technology.