Artificial intelligence is often explained with abstract examples: cats and dogs, handwritten digits, or large public image datasets. These examples are useful, but they do not show how AI can be taught in a technical radar environment. In this short video, we demonstrate how FreeScopes AI turns real radar measurements into a practical machine-learning exercise: students collect and label data from three radar targets, train a neural network, and use it to classify the measured objects.

Artificial intelligence is becoming increasingly relevant in radar, electronic warfare, and spectrum analysis. Machine learning methods can support pattern recognition, anomaly detection, signal classification, and decision support in complex electromagnetic environments.

 FreeScopes AI I introduces neural networks through radar signal analysis. Build, train, and evaluate AI models visually without programming.

FreeScopes AI enables engineers to build and deploy AI for radar and sensor data without coding. A short demonstration shows rapid object classification in practice.

With the release of FreeScopes AI 1, SkyRadar introduces a structured framework for applying artificial intelligence to radar-based target detection and classification. The system is designed to bridge the gap between raw radar signals and operationally relevant insight, following a transparent and reproducible processing chain.

This article discussed why AI skills are critical for ATSEP and radar operators, and how SkyRadar’s FreeScopes AI delivers hands-on training for future-ready qualification.

In light of the rapidly changing geopolitical landscape, Europe is taking bold steps to reinforce its defense readiness—its Verteidigungsfähigkeit. This strategic pivot demands more than just modernized equipment; it calls for innovation and comprehensive qualification of military personnel. SkyRadar is ready to play its part.

The integration of Artificial Intelligence (AI) in Air Traffic Management (ATM) is rapidly transforming the way airspace is monitored, optimized, and secured. However, for AI to be effectively implemented in ATC (Air Traffic Control) and ATSEP (Air Traffic Safety Electronics Personnel) operations, a centralized data and cloud architecture is essential.

In today’s rapidly evolving threat landscape, the ability to counter electronic warfare and ensure accurate radar operations is paramount for military forces. AI-driven radar systems offer a transformative edge in detecting, classifying, and localizing targets, even under conditions of severe interference. The FreeScopes Environment provides military academies with the tools to train radar officers in these advanced technologies, ensuring readiness for modern defense challenges.

Air traffic control (ATC) is the backbone of modern aviation, ensuring safety, efficiency, and seamless coordination in increasingly crowded skies. As AI reshapes the landscape of radar systems, ATC academies must adapt their training programs to prepare Air Traffic Safety Electronics Personnel (ATSEP) for the challenges and opportunities ahead. The FreeScopes Environment offers a comprehensive AI training solution designed to future-proof ATC operations.

Radar technology plays a critical role in air traffic control (ATC) and military operations, ensuring safety, efficiency, and strategic decision-making. The integration of artificial intelligence (AI) into radar systems is transforming these fields, offering enhanced capabilities in target detection, classification, and localization—even under challenging conditions such as electronic interference. This article summarizes the key advancements demonstrated in our forthcoming series of articles and videos, emphasizing their relevance to Air Traffic Safety Electronics Personnel (ATSEP) and military radar officers.

In this article, we will explore the deployment of the Spot Jamming DetectionNet into the FreeScopes Environment, showcasing its ability to detect targets and determine their exact positions under challenging jamming conditions.

In this article, we will explore the deployment of our trained TargetClassificationNet into the FreeScopes Platform and demonstrate its effectiveness in real-time target classification.

In this article we will delve into the architecture, features, and training process of the Spot Jamming DetectionNet developed for the FreeScopes environment. This specialized model is designed to detect spot jamming in radar systems and accurately localize targets, even under challenging conditions of intentional interference.

Mastering the application of artificial intelligence (AI) in radar technology is becoming an essential skill for Air Traffic Safety Electronics Personnel (ATSEP) and military radar operators. By understanding the intricacies of AI-driven radar systems, trainees can develop the expertise needed to enhance target detection, localization, and classification—key components of modern air traffic management and defense strategies. The FreeScopes platform offers a unique, hands-on approach to achieving these learning outcomes through its sophisticated model architecture, which we will outline in this article.

FreeScopes AI 1 equips ATSEP trainees with skills in target detection, data interpretation, and anomaly analysis for effective AI-based air traffic management.

As air traffic control (ATC) systems evolve, the integration of artificial intelligence (AI) is becoming critical to improving efficiency, safety, and decision-making. However, for AI to work effectively in air traffic management (ATM), a centralized data infrastructure and Service Monitoring & Control (SMC)-enabled applications are necessary. These technologies are not just reshaping ATC systems, but they are also transforming the roles and responsibilities of Air Traffic Safety Electronics Personnel (ATSEP). This blog explores the foundational role of centralized data and service-based architectures, and how AI will shape the future of ATSEP in the context of AI-enhanced ATC systems.

As global air traffic continues to increase, the complexity of managing airspace grows more challenging. To address these complexities, Air Traffic Control (ATC) systems are turning to emerging technologies such as Artificial Intelligence (AI), cloud computing, and cybersecurity to maintain safety and efficiency. Integrating these technologies is not just an opportunity—it is a necessity. This article provides an introduction to how AI, supported by robust cloud architecture and underpinned by strong cybersecurity measures, can transform ATC into a more scalable and secure system while ensuring compliance with data privacy regulations.

Artificial intelligence (AI) has significantly transformed many industries, including air traffic management. For Air Traffic Safety Electronics Personnel (ATSEP), AI presents an opportunity to improve daily tasks and processes, boosting efficiency, safety, and reliability. However, this transition necessitates a thoughtful and strategic approach to maintain the integrity of air traffic systems.

Processing errors in AI analytics within air traffic control can jeopardize system accuracy, safety, and efficiency. This article explores causes, impacts, and remedies, emphasizing the need for rigorous testing and human oversight to mitigate risks.

This article sheds light on the potential roles of Artificial Intelligence in improving Cybersecurity in times of data-driven and digitalized Air Traffic Management.

New ATM system generations will be more open and flexible, making them a target for cyber attacks and terrorism. This article discusses solutions for ATSEP basic and on-the-job education on cyber-security.

The world is accelerating. Technology is propelling. To stay in the forefront of innovation and the market, companies need to embrace AI, platforms and the crowd. But to be good, they also need to focus on their core competences. So let's team up.

In the beginning of the 2000s, the SAP's research director told me IoT will be the next big thing. Now it's here. So let's make money with it - but beware the wrong evangelists. But where is our role?

After the Internet of Pages and the Internet of Services, now the Internet of Things is emerging. This article looks at the central role that radar is expected to find in many IoT applications.

Artificial Intelligence is disruptively changing the world of Aviation and IoT. Radar sensor data play a key role as they are in contrast to photographic images quantitative and measurable. SkyRadar developed a course providing hands-on experiments for Artificial Intelligence. 

This article will give an overview of the basic and fundamental notions of ML. It is part of a series developed for practitioners. The goal is to be rapidly able to apply and make use of ML.

This document describes SkyRadar's 2 years course in A.I, including elements of data science and connected sciences for undergraduate courses (Bachelor Programs).

The ID3 - or ID3 (Iterative Dichotomiser 3) - is a supervised classifier based on decision tree learning methodology. The ID3 classifier generates a decision tree from a set of data (the training data) which can be used to classify new data.

In this article, we shall detail the main results and implementation regarding the processing and classification of RADAR objects using Machine Learning (M.L) techniques such as Hidden Markov Models, Neural Networks or others.

In what follows we will present examples of Deep Learning networks and detail their various designs. It is a detailed tutorial, written for students and engineers who want to acquire a profound understanding of the subject.

In this tutorial, we will have a detailed look at one of the most powerful classes of machine learning and Artificial Intelligence algorithms that exists: the Bayesian Classifiers.

SkyRadar's NextGen Pulse Radar operates in the x-band, just like en-route radars in ATC, as well as military and marine radars. It includes all the important features needed by ATCOs and ATSEPs and reaches up to Artificial Intelligence. Its modular structure allows to customize it on university requirements as well.

In the first part of this series, we introduced the general concepts needed for understanding the Hidden Markov Models Classifiers. Namely: Bayesian Logic, The concept of Bayesian Classifiers and Bayesian networks. All these notions are now grouped to form a new type of classifier which can accurately model and classify time-series data such as the RADAR data.

In this article we will explore a very special class of classifiers, the Hidden Markov Model Classifiers (HMMs). They are mainly a statistical and probabilistic model but they have found their entry in the world of Machine Learning since they can be trained and classify data.

The recent crash of Ukrainian Airlines Flight 752, downed by a surface-to-air (SAM) Iranian missile after a tragic human error from a military RADAR operator underlines dramatically the need for ADS-B, combined with automatic computer-based recognition of RADAR targets.

In this article we shall perform the anatomy of a simple but efficient convolutional network (CNN), the LeNet-1 neural network.

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.

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.

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.

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.