Tools, Software, and Datasets

The open-source and commercial ecosystem for RF analysis: signal processing libraries, SDR control software, specialized analysis tools, the SigMF ecosystem, public datasets, automation APIs, and the cables and connectors that tie a bench together.

Open-Source Signal Processing

• GNU Radio: signal-processing flow graph framework. The de facto open standard for SDR work. Available for Linux, Mac, Windows.
• scikit-rf: Python library for RF/microwave network analysis (S-parameters, network synthesis).
• scipy.signal: general-purpose DSP within scientific Python.
• NumPy: foundational array math for any DSP work in Python.
• MATLAB Signal Processing Toolbox: commercial alternative; widely used in academia and industry.
• Octave: free MATLAB-syntax-compatible alternative.

SDR Control Software

• GQRX: spectrum monitoring with RTL-SDR, USRP, and others.
• SDR# (SDRSharp): Windows-focused SDR control.
• SDR Console: Windows-focused multi-SDR.
• CubicSDR: cross-platform SDR with audio decoding.
• OpenWebRX: web-based SDR sharing.

Specialized Analysis Tools

• Inspectrum: graphical I/Q analysis.
• Universal Radio Hacker: protocol reverse engineering of unknown wireless signals.
• rtl_433: decoder for hundreds of 433/315/868/915 MHz devices.
• Wireshark with Bluetooth/Wi-Fi plugins: protocol-layer analysis.

SigMF (Signal Metadata Format)

• sigmf Python package: read/write SigMF files.
• SigMF Specification: open standard at github.com/sigmf/SigMF.
• Supported by GNU Radio, scikit-rf, RTSA Suite PRO, and others.

Aaronia RTSA Suite PRO Modules

• Block-based signal processing pipeline editor.
• Custom triggers and FMTs.
• AI classification plugins.
• Modulation-specific demodulators (BLE, Wi-Fi, 5G NR, LoRa).
• Scripting hooks (Python, C++).
• KML / KMZ / GeoJSON / NMEA / MQTT / REST export.

Public Datasets and SigMF Archives

• DARPA Spectrum Collaboration Challenge: extensive RF dataset from a multi-year competition.
• RadioML 2018 / 2024 datasets: ML training data with labeled modulations across many SNR levels.
• DeepSig RFML Dataset: similar ML benchmarking.
• Galaxy Radio Survey archives: astronomical I/Q data, useful for noise characterization.
• GFTS (Global Free-To-Air Spectrum): opportunistic captures of broadcast and ISM bands.
• ETSI test vector sets: standard waveforms for compliance verification.

APIs and Automation

• SCPI / VISA: standard programmatic instrument control.
• pyvisa: Python binding for VISA.
• NI-VISA: National Instruments VISA implementation.
• Aaronia API: Python and C++ APIs for SPECTRAN V6 PLUS.
• MATLAB Instrument Control Toolbox: bench instrument automation.
• Aaronia REST API and MQTT publisher: integration with broader systems.

Cloud and Big-Data Platforms

• AWS S3 / Snowflake / Databricks: large-scale I/Q archive and analysis.
• HDFS / Spark: open-source big-data processing.
• Apache Kafka / RabbitMQ: streaming I/Q to multiple consumers.
• TensorFlow / PyTorch / scikit-learn: ML for RF classification.

Common Connectors and Cables

Cable Types and Loss

Closing Note

These appendices are designed to be referenced repeatedly. The glossary lookup, math formulas, product comparisons, quick-reference tables, quiz keys, education pathways, employer lists, standards summaries, and tool ecosystem entries all reduce friction in daily work. A book that lives on the bench earns its keep through these reference sections more than through any single read of the main chapters.