Signal Processing & RF Ecosystem
Experimental projects exploring SDR spectrum monitoring and biometric signal processing.
Software Defined Radio
Project Spectra — Autonomous Spectrum Monitoring
Phase 9/12 — v2.0 Signal Intelligence · Full Details →
What It Is: A distributed spectrum monitoring system that autonomously scans radio bands, classifies signals using ML, and maintains a persistent “Signal Census” database. Raspberry Pi edge nodes stream IQ samples to a Mac mini core for processing, classification, and visualization.
Spectra Rust TUI — live spectrum, Kitty graphics waterfall, and device controls.
Key Features:
- Two-stage ML classification pipeline (real-time + retrospective)
- Signal Census database (DuckDB) with anomaly detection
- Autonomous missions: bandplan sweeping, satellite pass scheduling
- Direction finding via Kraken SDR (5-channel coherent receiver)
- WebGL waterfall frontend, Rust TUI, and REST API
- v2.0 WebSocket Foundation: Multiplexed WebSocket streaming, dual-mode TUI, web-based visualization
Current Status: v1.0 complete (58/58 plans). v2.0 Phase 9 in progress (11/55 plans complete) — central server architecture with WebSocket streaming and signal intelligence overlay.
Tech Stack: Python, Rust, TypeScript, FastAPI, React, DuckDB, PyTorch, Skyfield, WebSocket
Multi-SDR Streaming Server
Phase 3/4 — TUI & Live Config (90%) · Full Details →
What It Is:
A single Rust binary that auto-detects every connected SDR (RTL-SDR, AirSpy HF+, AirSpy), streams each over the standard rtl_tcp protocol, and provides a TUI dashboard and HTTP API for monitoring and control.
Key Features:
- Auto-detection and concurrent streaming from RTL-SDR, AirSpy HF+, and AirSpy devices
- TUI dashboard with live frequency, gain, and sample rate adjustment over SSH
- HTTP REST API for device status, health checks, and programmatic monitoring Current Status: Phase 3 at 90% — TUI dashboard, interactive controls, and HTTP API complete. Config save/reload remaining.
Tech Stack: Rust, tokio, axum, ratatui
Illumination Reflection Tracking
Phase 6/10 — Track Management · Full Details →
What It Is: Analyzes reflections of existing broadcast transmissions using coherent multi-channel SDR hardware (KrakenSDR). Distributed multi-beam system with parallel surveillance channels for real-time aircraft tracking.
Current Status: Phase 6 — implementing track management and association logic. Phase 5 (CFAR detection) complete.
Tech Stack: Python, numpy, scipy, ProcessPoolExecutor
Wi-Fi Signal Reflection — Through-Wall Detection
Phase 5 — Complete · Full Details →
What It Is: Coherent SDR array processing for through-wall human detection via Wi-Fi signal reflection and phase analysis.
Current Status: Phase 5 complete — proof-of-concept detection operational
Tech Stack: Python, Signal Processing
Audio Processing
soundarray — Spatial Audio Processing
Active Development · Full Details →
What It Is: An exploration-focused audio processing system using Raspberry Pi and microphone arrays. Focuses on spatial audio (ToA, beamforming) and classification (vehicles, wildlife) using an “analyst” agent approach.
Key Features:
- Time of Arrival (ToA) Estimation: Localize sound sources using microphone array phase differences
- Beamforming: Directionally filter audio to enhance signals from specific angles
- Sound Classification: Classify vehicles and wildlife by acoustic signatures
- Agent Integration: Provides structured insights to analyst agent frameworks
Current Status: Exploring hardware options (ReSpeaker, Matrix arrays) and beamforming algorithms
Tech Stack: Python, numpy, scipy, PyTorch/TensorFlow (classification)
Health & Biometrics
HRV Monitor — Real-Time Heart Rate Variability
Active Development · Full Details →
What It Is: Rust-based BLE driver for heart rate variability monitoring. Connects to consumer HRV sensors (Elite HRV CorSense, Morpheus M7), streams raw RR intervals, computes time-domain metrics (RMSSD, SDNN, pNN50, AVNN) in real-time, and logs sessions to Parquet files for analysis.
Key Features:
- Real-Time Metrics: Live HRV computation with rolling 60-second windows
- BLE Streaming: Direct connection to standard Bluetooth Heart Rate Profile devices
- Terminal Dashboard: TUI with live charts and metric visualization
- Session Logging: Automatic Parquet export for DuckDB/Polars analysis
Current Status: Core functionality complete, Linux support and frequency-domain metrics planned
Tech Stack: Rust, btleplug, ratatui, cardio-rs, Apache Arrow/Parquet
HealthyPi Biometric Signal Processing
Phase 6/6 — Apple Ecosystem (v1.0, 87%) · Full Details →
What It Is: Modular signal processing ecosystem using the HealthyPi biometric hardware platform (developed by Protocentral) for ECG, PPG, respiration, and EEG analysis with NeuroKit2.
Key Features:
- Virtual patient simulator for development without hardware
- NATS message bus integration for agent coordination
- Pydantic models for ECG, PPG, EDA, EEG, IMU, and respiration
- HealthyPiKit Swift package for Apple ecosystem integration
Current Status: Phase 6, Plan 2/7 (87% complete) — Apple ecosystem integration. Phases 1-5 complete and verified.
Tech Stack: Python, Swift, NeuroKit2, numpy, scipy, NATS, Pydantic