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.
This is the second article in our series, introducing applied AI qualification for ATSEP and military surveillance experts. Find the first article here.
Tailored AI Benefits for Civil and Military Radar Operations
AI-driven radar technology offers distinct advantages for civil and military radar professionals, addressing their unique operational challenges and requirements.
For civil ATSEP professionals, the FreeScopes Environment supports enhanced radar-based decision-making, ensuring seamless air traffic management in increasingly complex airspaces. By training with AI-powered tools, ATSEP gain the skills to deploy, monitor, and improve systems capable of classifying and localizing targets with unmatched precision. This translates into safer skies, improved efficiency, and compliance with evolving regulatory standards.
For military surveillance officers, the stakes are often higher, involving electronic warfare, spot jamming, and other adversarial conditions. The FreeScopes Environment equips military personnel to counteract interference, reliably detect and track targets, and optimize mission-critical radar systems. With the ability to evaluate and adapt AI tools to their needs, military operators enhance their readiness to respond to rapidly changing tactical environments.
By addressing the specific needs of both civil and military radar professionals, the FreeScopes toolset ensures that trainees in both domains can achieve operational excellence, whether safeguarding commercial air traffic or securing strategic defense objectives.
A Technical Deep Dive for Practitioners
This article provides a detailed technical overview of AI-driven radar systems within the FreeScopes Environment, tailored for operational practitioners such as ATSEP and military surveillance officers. For decision-makers, strategic planners, and those seeking a broader perspective on the managerial and visionary implications of AI in radar technology, we offer a variety of articles that explore strategic applications, long-term benefits, and the future of AI in civil and military radar operations. These resources provide insight into how AI can reshape operational efficiency, security, and training paradigms in the aviation and defense sectors.
Overview of the FreeScopes Model Architecture
The FreeScopes platform is designed to handle complex radar data workflows, from initial input to target classification. At its core, the system processes recorded radar data and associated position labels through a meticulously structured pipeline, ensuring robust and accurate results.
Input and Preprocessing
The workflow begins with the LoadInputData block, responsible for loading radar data and position labels. This data is then processed through the PreProcessInputData block, where two critical transformations occur:
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Normalization: The IQ data generated by the radar simulator is scaled appropriately to ensure compatibility with the model.
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Gaussian Labeling: Position labels are converted into Gaussian distributions, smoothing the target location information and improving localization accuracy.
Core Model Structure
The processed data is fed into the model, which comprises several specialized components:
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Conv1D Block: This block extracts spatial features using a 1-dimensional convolutional kernel, identifying patterns essential for classification and localization.
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Flatten Block: The data is reshaped into a format suitable for dense layers.
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Dense Layers: These layers learn high-level representations of the radar data features.
Parallel Branches for Specialized Tasks
The model architecture splits into two branches, each addressing a specific task:
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Target Localization:
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The first branch further processes the data through a dense layer before reaching the PositionOutput block.
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The PositionOutput block produces a 400-element output, corresponding to positional resolution at 0.05-meter intervals over a 20-meter range.
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A softmax activation function ensures that the output represents a probability distribution, with peaks indicating target positions.
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Target Classification:
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The second branch leads to the ClassificationOutput block via another dense layer.
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This block produces a 3-element output, categorizing targets as:
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Class 0: No target present.
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Class 1: Target with low radar cross-section (RCS).
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Class 2: Target with high RCS.
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Softmax activation ensures that the probabilities are well-calibrated for accurate classification.
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Training and Performance Metrics
The training process is as methodical as the architecture itself. Over ten epochs, the model’s loss function decreases consistently, indicating effective learning. The output console provides key metrics, including accuracy improvements and a confusion matrix that summarizes classification performance. The matrix highlights the model's ability to differentiate between target classes and offers valuable insights for further refinement.
Benefits for ATSEP and Military Training Academies
The dual functionality of the FreeScopes model—localization and classification—is particularly relevant for Air Traffic Safety Electronics Personnel (ATSEP) and military training academies. For ATSEP, the system enhances radar-based decision-making, critical for maintaining air traffic safety. In military contexts, the ability to distinguish between high-RCS and low-RCS targets supports tactical planning and threat assessment.
SkyRadar is the only supplier offering hands-on training for AI in radar surveillance, a distinct advantage for training academies. This hands-on approach enables trainees to:
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Develop competencies to comprehend, apply, evaluate, select, and use AI solutions in radar systems.
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Accurately judge system performance and detect potential compromises.
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Gain expert-level knowledge to make informed decisions on system improvements.
By actively engaging with AI tools and techniques, trainees build not only theoretical understanding but also practical expertise, equipping them to meet the demands of modern radar operations confidently.
Watch the Video
The Right Tool for ATSEP Training and Military Training Academies
The integration of AI into radar systems, as demonstrated by the FreeScopes platform, offers transformative potential for training academies in both civil and military domains. By automating target detection, localization, and classification, this technology not only improves efficiency but also provides a foundation for future advancements in radar training and operation. For a deeper understanding of this model and its capabilities, we encourage you to explore the detailed video accompanying this article.
Stay Tuned
Stay connected with our ongoing publications on Artificial Intelligence as we continue to explore its impact on Air Traffic Control. Our aim is to contribute to the evolving conversation around AI’s role in ATC, particularly the shifting responsibilities and qualifications of ATSEP. This will help ensure readiness for the challenges ahead in this important field.
