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.
FreeScopes AI starts with the systematic collection and labeling of radar data, ensuring that training datasets reflect realistic operational conditions. This data is then cleaned, normalized, and augmented to improve robustness and reduce bias. On this foundation, machine-learning models are developed and trained, with close attention to architecture choice, training behavior, and validation results. Subsequent optimization and testing focus on real-time constraints, ensuring that model performance remains stable when transferred from laboratory conditions to live environments.
The final step integrates the trained models directly into the FreeScopes environment as deployable software blocks. This allows AI functions to be embedded into existing radar training, analysis, and experimentation workflows without disrupting established system architectures. FreeScopes AI is therefore positioned not as a black box, but as an auditable and adaptable tool for radar professionals.
The following video presents the FreeScopes AI concept, illustrating the end-to-end workflow from radar data acquisition to AI deployment within FreeScopes.
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