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.

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.

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.