WASABI (Wave Analyzer for Signal Anomaly and Behavior Inspection) is an RF fingerprinting workspace that covers the complete workflow from signal exploration and fingerprint learning to transmitter identification.
Import captured RF signals, inspect individual bursts, build fingerprint models for registered transmitters, and use the trained models to identify incoming signals. WASABI brings the analysis, training, evaluation, and detection workflow together in one place, making it easier to develop and validate RF fingerprinting systems from real-world signal data.
Explore captured RF signals at both dataset and burst level. Inspect waveform characteristics, spectra, envelopes, and other signal features before building a fingerprint model.
Organize RF samples by transmitter and prepare labeled training data directly within the workspace, turning raw signal captures into datasets suitable for RF fingerprint learning.
Train models to learn subtle transmitter-specific characteristics embedded in RF signals. Compare training results and iterate on datasets and model settings within the same workflow.
Evaluate how well a trained fingerprint model distinguishes registered transmitters before deployment. Review classification results and identify where additional data or model refinement may be needed.
Apply trained fingerprint models to incoming RF bursts and identify the most likely transmitter in real time, with confidence scores and candidate probabilities for each detection.
RF fingerprinting involves more than running a classifier. Signals must be inspected, bursts prepared, transmitters labeled, models trained and evaluated, and finally applied to new observations. WASABI brings the entire RF fingerprinting workflow into one workspace, from captured signals to transmitter identification.
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