FreeScopes AI I
Classification, anomaly detection and visual neural-network training.
FreeScopes AI II
FreeScopes AI II extends the AI training introduced in AI I toward more complex radar perception tasks. Trainees work with radar and simulator datasets across multiple frames and study how AI methods can interpret targets, changing signal conditions and jamming-related effects.
The module connects radar perception, temporal consistency and jamming awareness while preserving the distinction between precisely labelled simulator scenarios and observation-based annotations from real radar measurements.
Designed for advanced radar and AI education in civil and defence-oriented training environments, universities and research laboratories.
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AI I introduces classification, anomaly detection and the practical neural-network training workflow. AI II moves into more complex radar perception, where information may span successive frames and where signal quality or interference conditions can change over time.
Trainees can work with structured radar representations such as range-Doppler data, detection outputs or radar image-like representations where these are included in the delivered training configuration.
Simulator data can provide complete knowledge of target states and explicitly injected jamming parameters. This allows precise labels for training and evaluation.
Real radar measurements are different. They do not provide true physical ground truth. Training therefore relies on observation-based or derived annotations such as detection results, track consistency and identified jamming events.
Radar & DSP Foundation → AI I → AI II
AI II is the advanced continuation of AI I. It is not part of the linear ATC processing path and does not replace the separate FreeScopes or SkySim ECCM products.
Supported models, exact workflows, datasets, explainability methods and hardware or server dependencies remain delivery-specific and require confirmation.
Trainees can explain the progression from static classification to multi-frame radar perception; distinguish simulator ground truth from observation-based real-radar annotations; assess temporal consistency and scene reliability; describe jamming-aware classification boundaries; and prepare detection outputs for later tracking-oriented training concepts.
Classification, anomaly detection and visual neural-network training.
Controlled radar scenarios, datasets and explicitly injected parameters where included.
Student-side disturbance detection, analysis and filtering training.
Advanced student-side analysis for corresponding ECCM training scenarios.
Use FreeScopes AI II to explore multi-frame observations, scene reliability and jamming-aware radar analysis in a controlled training environment.
SkyRadar develops innovative radar training solutions and simulation systems, empowering education, research & professional training in aviation and defense sectors.
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