SkyRadar Blog

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 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.

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.

Radar systems face various threats from electronic warfare (EW), with radar jamming being one of the most effective ways to disrupt their performance. Through techniques like barrage jamming, spot jamming, and deception jamming, adversaries can compromise the radar’s ability to detect and track targets. SkySim’s EW Extension provides an advanced simulation platform where users can explore these jamming techniques and learn how to counter them with electronic counter-countermeasures (ECCM), supported by FreeScopes Disturbance Filtering & Analysis.

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.

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.

SkyRadar offers comprehensive Radar Laboratories designed specifically for Radar Officers and Maintenance Personnel. This article presents a straightforward modular configuration to get started.

The following video explores the Doppler Phase Shift Enhanced Disturbance Filter, a sophisticated approach designed to improve radar accuracy and effectively detect jamming.

The Doppler Phase Shift Enhanced Disturbance Filter (EDF) is an advanced radar signal processing system designed to effectively handle jammed radar data. It utilizes sophisticated algorithms to distinguish genuine radar reflections from jamming signals, thereby enhancing the radar's ability to accurately detect and track targets even in hostile environments.