SkyRadar Blog

RADAR data classification mainly relies on convolutional neural networks. In this article, we shall detail and explain the main operations performed by Convolution networks in order to classify RADAR data.

A relevant technique to classify RADAR object is to use a colored 2D map representing range, speed or frequency against time and color the map with power intensity. Here we represent examples of such typical RADAR data.

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.

How do millennials learn and why does this matter to aviation academies? This article looks at the specifics of the younger generation. What drives them and what are their specific learning patterns? And how do aviation academies need to adapt to attract the best of them and to qualify them most effectively?

RADAR has become a key technology to help navigate aircraft. With a growing demand of efficient air space usage, the demand for RADAR systems is growing. RADAR is a technology that uses Radio waves to detect objects in the air or sea. 

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.

It is projected that by 2036, the aviation sector will need 125,000 air traffic controllers, and 1.3 million aircraft maintenance personnel to fill vacancies left by retiring aviation professionals. In 2016, the Next Generation of Aviation Professionals (NGAP) Programme was formalized as an ICAO initiative to meet the need for trained and qualified aviation professionals.

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.

More and more users want to access our radar raw data and read out IQ data directly. Having the raw data in a well digestible data format makes it easy to do the further treatment in any numerical computing environment. Research on new algorithms on real-time data becomes comfortable and powerful.