SATx - Satellite Detection, Tracking, and Transmission System

A complete automated system for detecting, tracking, decoding transmissions from satellites passing overhead. SATx leverages SDR hardware, GNU Radio, and machine learning to provide end-to-end satellite observation capabilities with 100% operational reliability.

🖥️ Web Dashboard

SATx features a modern, professional web interface for real-time monitoring and control:

Main Dashboard

Satellite Tracking

Analytics View

Dashboard Features:

• 🎨 Modern dark theme with animated space background
• 📊 Real-time signal candidate monitoring
• 🛰️ Live satellite tracking with pass predictions
• 📈 Analytics and detection statistics
• 📁 Recording library management
• 🔄 Auto-refresh every 30 seconds
• 📱 Fully responsive design

Access the dashboard at http://localhost:8080 after starting the web server.

Table of Contents

• Features
• System Status
• ML and AI Capabilities
• Quick Start
• Hardware Requirements
• Software Requirements
• Installation
• Configuration
• Usage
• Testing
• Project Structure
• API Reference
• Contributing
• License
• Resources

Features

• Automatic TLE fetching and pass prediction - Real-time orbital data and pass scheduling
• Doppler-corrected SDR recording - High-quality I/Q data capture with frequency correction
• ML-based signal detection - CNN-powered signal identification and classification
• Multi-protocol decoding - Support for AX.25, AFSK, GMSK, BPSK, QPSK, and more
• SatNOGS integration - Community network validation and data sharing
• Automated scheduling and processing pipeline - End-to-end observation automation
• Professional web dashboard - Modern, responsive UI with real-time monitoring
• 🆕 Wide-band spectrum scanner - Advanced scanning with signal identification
• 🆕 Signal identification system - Automatic protocol and modulation detection
• 🆕 Ghost Mode privacy protection - Bulletproof operational security and anonymity
• Optional transmission capabilities - Authorized transmission with safety protocols
• Docker containerization - Easy deployment and scaling
• Comprehensive testing suite - Automated validation of all components

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🚀 New to SATx? Check out the Quick Start Guide to get up and running in minutes!

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System Status

Current Status: Production Ready

• Test Coverage: 100% (38/38 tests passing)
• Operational Status: Fully functional
• Documentation: Complete requirements and setup guides
• Hardware Compatibility: RTL-SDR, Airspy, SDRplay, HackRF, USRP

For detailed system status and reports, see the Critical Requirements Document.

ML and AI Capabilities

SATx uses deep learning for advanced signal processing:

• Signal Detection: CNN-based spectrogram analysis for automatic signal identification
• Protocol Classification: Multi-class classification for different modulation schemes
• Noise Reduction: AI-powered filtering and enhancement
• Training Pipeline: Automated data preparation and model training
• SatNOGS Integration: Community data for improved model accuracy

Models are trained on both synthetic and real satellite signals, achieving high accuracy in various conditions.

Supported Frameworks

• PyTorch for primary ML operations
• TensorFlow/Keras for alternative implementations
• Scikit-learn for classical ML algorithms
• CUDA acceleration support for GPU processing

Quick Start

Automated Setup (Recommended)

For a complete turnkey installation with ML training:

# Clone and setup everything automatically
git clone https://github.com/Artifact-Virtual/satx.git
cd SATx
./setup.sh

The setup script will:

• Detect your OS and install dependencies
• Set up Python virtual environment
• Install SDR drivers and GNU Radio
• Download and train ML models
• Configure your station
• Test the complete pipeline

Manual Setup

1. Install dependencies:

   pip install -r requirements.txt

2. Configure your station: Edit configs/station.ini with your location coordinates

3. Fetch latest TLEs:

   python scripts/fetch_tles.py

4. Predict upcoming passes:

   python scripts/predict_passes.py

5. Run automated observation:

   python scripts/scheduler.py

ML Training Setup

To train or retrain the signal detection models:

# Prepare training data
python scripts/prepare_training_data.py

# Download additional data from SatNOGS
python scripts/download_satnogs_data.py

# Train the model
python models/model_v1/train.py

Hardware Requirements

Minimum Setup ($50-100)

• RTL-SDR dongle (RTL2832U + R820T2)
• DIY QFH or Turnstile antenna
• USB extension cable
• Coax cable + SMA adapters

Recommended Setup ($200-400)

• Airspy Mini or SDRplay RSP1A
• QFH antenna with LNA
• Az/El rotator system
• Quality coaxial cables and connectors

Professional Setup ($800+)

• HackRF One, LimeSDR, or USRP B210
• Full rotor system with controller
• Professional antenna system
• Dedicated computer/server with GPU

See the Critical Requirements Document for detailed hardware specifications and budget breakdowns.

Software Requirements

Core Dependencies

• Python 3.8+
• GNU Radio 3.8+
• RTL-SDR drivers
• Docker and Docker Compose

Optional Dependencies

• CUDA 11.0+ (for GPU acceleration)
• GPredict (for manual antenna control)
• gr-satellites (for additional decoders)

Supported Operating Systems

• Ubuntu 20.04 LTS or later
• Debian 11 or later
• CentOS/RHEL 8 or later
• Windows 10/11 (with WSL2)
• macOS 11+ (with Intel/Apple Silicon support)

Installation

Docker Installation (Recommended)

# Build and run with Docker Compose
docker-compose up -d

# View logs
docker-compose logs -f

Native Installation

# Install system dependencies
sudo apt-get update
sudo apt-get install python3 python3-pip git docker.io

# Clone repository
git clone https://github.com/Artifact-Virtual/satx.git
cd SATx

# Install Python dependencies
pip install -r requirements.txt

# Run setup script
./setup.sh

Configuration

Station Configuration

Edit configs/station.ini:

[station]
name = My Ground Station
latitude = 40.7128
longitude = -74.0060
altitude = 100
device = rtl=0
sample_rate = 2400000
frequency = 437800000

SDR Configuration

Configure SDR devices in configs/sdr.ini:

[sdr]
device_type = rtl
device_args = rtl=0
sample_rate = 2400000
gain = 40
bias_tee = true

ML Configuration

Configure ML models in configs/ml.ini:

[ml]
model_path = models/model_v1/
batch_size = 32
threshold = 0.8
use_gpu = true

Usage

Basic Operation

# Start the complete system
python scripts/scheduler.py

# Monitor via web interface
# Open http://localhost:5000 in your browser

Command Line Tools

# Fetch TLE data
python scripts/fetch_tles.py

# Predict passes
python scripts/predict_passes.py --lat 40.7128 --lon -74.0060

# Process recording
python scripts/process_recording.py recording.iq

# Run tests
python tests/run_all_tests.py

Web Interface

Start the web dashboard:

python3 web/app.py

The modern web dashboard provides:

• 📊 Real-time system monitoring - Live stats on recordings, candidates, and system status
• 🛰️ Satellite tracking - Active satellite positions and upcoming pass predictions
• 📈 Analytics dashboard - Detection statistics and performance metrics
• 📁 Recording management - Browse and manage your I/Q recording library
• 🎨 Beautiful UI - Professional dark theme with animated space background
• 🔄 Auto-refresh - Updates every 30 seconds automatically
• 📱 Responsive design - Works perfectly on desktop, tablet, and mobile

Access at: http://localhost:8080

Features:

• Tab-based navigation for different views
• Search and filter signal candidates
• Color-coded signal strength indicators
• Animated statistics cards with hover effects
• Real-time data visualization

🔍 Spectrum Scanner

Scan frequency bands to discover active signals:

# Scan all configured bands
python3 scripts/spectrum_scanner.py

# Scan specific band (137-138 MHz)
python3 scripts/spectrum_scanner.py --band 137.0-138.0

# Save results to file
python3 scripts/spectrum_scanner.py --output scan_results.json

Features:

• Wide-band frequency scanning
• Automatic signal detection
• Signal type identification (Weather satellite, Amateur, CubeSat, etc.)
• Modulation type detection (FM, FSK, BPSK, etc.)
• Protocol identification (APT, AX.25, SSTV, etc.)
• Configurable scan parameters

🕵️ Ghost Mode - Privacy Protection

Enable bulletproof privacy and operational security:

# Enable maximum privacy (bulletproof)
python3 scripts/ghost_mode.py --enable --level maximum

# Enable high privacy
python3 scripts/ghost_mode.py --enable --level high

# Enable standard privacy
python3 scripts/ghost_mode.py --enable --level standard

# Check status
python3 scripts/ghost_mode.py --status

# Clean all traces
python3 scripts/ghost_mode.py --clean

# Disable ghost mode
python3 scripts/ghost_mode.py --disable

Privacy Levels:

• Standard: Disables external services, uses anonymous identifiers
• High: Minimizes logging, disables metadata collection, clears location data
• Maximum (Bulletproof): Enables encryption, memory-only operation, no persistent logs, complete anonymity

Ghost Mode Features:

• 🚫 Disables all external service connections (SatNOGS, etc.)
• 🎭 Anonymous station identifiers
• 🗑️ Secure file deletion (3-pass overwrite)
• 🔒 Data encryption for sensitive information
• 💾 Memory-only operation mode (minimal disk writes)
• 📍 Location data anonymization
• 🛡️ No telemetry or tracking
• 🧹 Trace cleaning capabilities

Use Cases:

• Security research and testing
• Monitoring sensitive frequencies
• Operating in restricted locations
• Privacy-conscious satellite observation
• Research requiring operational anonymity

Testing

Run comprehensive system tests:

# Run all tests with detailed reporting
python tests/run_all_tests.py

# Run specific test categories
python -m pytest tests/unit/ -v
python -m pytest tests/integration/ -v
python -m pytest tests/e2e/ -v

# Generate coverage report
python -m pytest --cov=satx --cov-report=html

Test Results

• Unit Tests: Core functionality validation
• Integration Tests: Component interaction testing
• End-to-End Tests: Complete workflow validation
• Performance Tests: System throughput and latency

All tests generate comprehensive HTML and JSON reports in tests/reports/.

Project Structure

SATx/
├── scripts/                    # Core automation scripts
│   ├── fetch_tles.py          # TLE data fetching
│   ├── predict_passes.py      # Orbital pass prediction
│   ├── process_recording.py   # Signal processing with ML
│   ├── scheduler.py           # Automated observation scheduler
│   ├── prepare_training_data.py # ML training data preparation
│   └── test_system.py         # Comprehensive system testing
├── configs/                   # Station and service configurations
│   └── station.ini           # Station location and settings
├── data/                      # Data storage
│   └── tles/                 # TLE orbital data
├── decoders/                  # Signal decoders and flowgraphs
│   └── grc_flowgraphs/       # GNU Radio flowgraphs
├── models/                    # ML models for signal detection
│   └── model_v1/            # Current model version
├── services/                  # Docker services
│   ├── ml/                   # ML processing service
│   └── sdr/                  # SDR processing service
├── tests/                     # Comprehensive test suite
│   ├── unit/                 # Unit tests
│   ├── integration/          # Integration tests
│   ├── e2e/                  # End-to-end tests
│   └── reports/              # Test reports and results
├── web/                      # Dashboard UI
├── docs/                     # Documentation
│   └── critical_requirements.md # Hardware requirements
├── transmit/                 # Transmission capabilities
├── recordings/               # Recorded I/Q data (gitignored)
├── logs/                     # Operation logs and candidates
├── docker-compose.yml        # Docker orchestration
├── requirements.txt          # Python dependencies
├── setup.sh                  # Automated setup script
└── README.md                 # This file

API Reference

Core Modules

• scripts.fetch_tles - TLE data management
• scripts.predict_passes - Orbital calculations
• scripts.process_recording - Signal processing pipeline
• scripts.scheduler - Observation automation
• models.model_v1.model - ML signal detection
• web.app - Web dashboard application

Configuration Files

• configs/station.ini - Ground station parameters
• configs/sdr.ini - SDR device configuration
• configs/ml.ini - Machine learning settings

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/satx.git
cd SATx

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python tests/run_all_tests.py

Code Standards

• Python 3.8+ compatible
• PEP 8 style guidelines
• Comprehensive test coverage required
• Type hints encouraged
• Documentation strings required

License

This project is licensed under the MIT License - see the LICENSE file for details.

Resources

Official Documentation

• Critical Requirements Document
• API Documentation
• Setup Guide

External Resources

• CelesTrak - TLE data
• SatNOGS - Community network
• gr-satellites - Decoders
• Skyfield - Orbital calculations
• GNU Radio - Signal processing

Community

• GitHub Issues - Bug reports and feature requests
• GitHub Discussions - Community discussions
• SatNOGS Forum - Satellite observation community

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SATx - Bridging the gap between amateur satellite observation and professional signal intelligence.