ClarityAcross the Spectrum
Y-STN Spectrum Analysis
Y-STN is a unified RF data preparation and AI model training environment built to accelerate spectrum analysis and signal intelligence workflows.
Spectrum analysis is central to maintaining awareness and control within increasingly congested electromagnetic environments. Y-STN streamlines the path from raw IQ data to trained machine learning models by automating annotation, feature extraction, and model development within a single, traceable workflow. This integrated approach reduces manual effort, improves analytical consistency, and accelerates capability development. By enabling faster, more reliable signal understanding, Y-STN supports more informed decision-making and strengthens spectrum operations across complex and dynamic environments.
Y-STN Advantage
At Precise Systems, spectrum analysis is treated as a strategic enabler of electromagnetic advantage. Effective operations in today’s congested spectrum environment require real-time awareness, precise signal characterization, and confident decision support. By detecting, identifying, geolocating, and analyzing emitters, advanced analytics support Electronic Support, Electronic Attack, and Electronic Protection activities. This disciplined approach improves spectrum awareness, reduces interference risk, enhances emission survivability, and accelerates the delivery of high-confidence signal intelligence where it matters most.
Workflow Environment
Y-STN is an integrated RF data annotation, spectrum analysis, and machine learning training workflow fully aligned with the SigMF standard. The environment is anchored by two core components: the Annotator and the Trainer.
The Annotator
The Annotator imports and automatically annotates IQ data files, eliminating hours of manual effort. Built-in templates support frequency hopping, time division and multiplexing, and burst modes with both frequency lock and unbound hopping. Annotated regions are preserved as structured metadata files for downstream analysis and model development.
The Trainer
The Trainer environment transforms annotated RF data into structured, model-ready intelligence through an integrated spectrum analysis and machine learning pipeline.
Spectrum Analyzer
The Spectrum Analyzer parses metadata generated by the Annotator and extracts discriminating features from the RF dataset. Users can transform these features across multiple domains, including Time-Frequency and Cyclostationary, producing structured NumPy arrays ready for downstream machine learning workflows.
Model Trainer
The Model Trainer builds and optimizes machine learning models using the processed dataset. It supports multiple architectures, including ResNet (18, 34, 50, 101, 152) and YOLO (XL, L, M, S, Nano), within a flexible framework designed to accommodate additional and custom models. The environment includes extensive optimization controls and performance analytics to support disciplined model development and evaluation.