geoveil-cn0 0.3.3

GNSS CN0 Analysis Library - Interference, Jamming & Spoofing Detection

geoveil-cn0

High-performance GNSS CN0 (Carrier-to-Noise) Signal Quality Analysis Library

A Rust-powered Python library for analyzing GNSS signal quality from RINEX observation files. Detect interference, jamming, spoofing, and multipath effects with research-grade algorithms.

Features

• 🛰️ Multi-GNSS Support: GPS, GLONASS, Galileo, BeiDou, QZSS, NavIC
• 📊 CN0 Analysis: Signal strength statistics, timeseries, and quality scoring
• 🚨 Threat Detection: Jamming, spoofing, and interference indicators
• 📈 Anomaly Detection: Configurable sensitivity for signal anomalies
• 🗺️ Skyplot Data: Satellite positions with azimuth/elevation (requires NAV file)
• ⚡ High Performance: Rust core with Python bindings via PyO3
• 📁 RINEX Support: Parse RINEX 2.x, 3.x, and 4.x observation files
• 🧭 Navigation Files: BRDC broadcast ephemeris and SP3 precise orbits

Installation

pip install geoveil-cn0

Quick Start

import geoveil_cn0 as gcn0

# Create analysis configuration
config = gcn0.AnalysisConfig(
    min_elevation=5.0,           # Elevation mask (degrees)
    time_bin_seconds=60,         # Time binning for statistics
    detect_anomalies=True,       # Enable anomaly detection
    anomaly_sensitivity=0.3,     # Sensitivity (0-1, lower = fewer false positives)
    interference_threshold_db=8.0,  # CN0 drop threshold for interference
)

# Create analyzer
analyzer = gcn0.CN0Analyzer(config)

# Analyze observation file (with optional navigation for skyplots)
result = analyzer.analyze_with_nav("observation.rnx", "navigation.rnx")

# Access results
print(f"Quality Score: {result.quality_score.overall}/100 ({result.quality_score.rating})")
print(f"Average CN0: {result.avg_cn0:.1f} dB-Hz")
print(f"Jamming Detected: {result.jamming_detected}")
print(f"Spoofing Detected: {result.spoofing_detected}")
print(f"Anomalies: {result.anomaly_count}")

# Get detailed data
for constellation in result.constellations:
    stats = result.get_constellation_summary(constellation)
    print(f"{constellation}: {stats['satellites_observed']} sats, CN0={stats['cn0_mean']:.1f} dB-Hz")

# Export to JSON
json_data = result.to_json()

Quality Score Components

The quality score (0-100) is computed from multiple factors:

Component	Weight	Description
CN0 Quality	35%	Signal strength relative to thresholds
Availability	20%	Satellite availability vs expected
Continuity	20%	Data continuity (gaps, cycle slips)
Stability	15%	CN0 variance over time
Diversity	10%	Multi-constellation coverage

Threat Detection Thresholds

Based on research from ITU-R M.1902-1, Stanford GPS Lab, and GPS Solutions journal:

• Jamming: Rapid CN0 drops >6 dB in <3 seconds
• Spoofing: Abnormally uniform CN0 (std <2 dB) or elevated average
• Interference: CN0 degradation >4 dB from baseline (ITU I/N=-6dB criterion)

API Reference

AnalysisConfig

config = gcn0.AnalysisConfig(
    min_elevation=5.0,              # Elevation cutoff in degrees
    time_bin_seconds=60,            # Time bin for statistics
    systems=['G', 'R', 'E', 'C'],   # GNSS systems to analyze
    detect_anomalies=True,          # Enable anomaly detection
    anomaly_sensitivity=0.3,        # 0-1, lower = stricter
    interference_threshold_db=8.0,  # dB drop for interference flag
    verbose=False,                  # Print debug info
)

CN0Analyzer

analyzer = gcn0.CN0Analyzer(config)

# Analyze without navigation (no skyplots)
result = analyzer.analyze_file("observation.rnx")

# Analyze with navigation (enables skyplots and elevation filtering)
result = analyzer.analyze_with_nav("observation.rnx", "navigation.rnx")

AnalysisResult

Property	Type	Description
quality_score	QualityScore	Overall quality metrics
avg_cn0	float	Mean CN0 in dB-Hz
cn0_std_dev	float	CN0 standard deviation
min_cn0, max_cn0	float	CN0 range
jamming_detected	bool	Jamming indicator
spoofing_detected	bool	Spoofing indicator
interference_detected	bool	Interference indicator
anomaly_count	int	Number of anomalies
constellations	list	Available constellations
duration_hours	float	Observation duration
epoch_count	int	Number of epochs

Methods

# Get constellation-specific statistics
stats = result.get_constellation_summary("GPS")  # Returns dict

# Get anomaly list
anomalies = result.get_anomalies()  # Returns list of dicts

# Get timeseries data for plotting
ts_data = result.get_timeseries_data()  # Returns dict with timestamps, cn0_mean, etc.

# Get skyplot data (requires navigation file)
skyplot = result.get_skyplot_data()  # Returns list of satellite traces

# Export to JSON
json_str = result.to_json()

Jupyter Notebook Widget

A ready-to-use interactive widget is included:

# In Jupyter notebook
exec(open("geoveil_cn0_gui.py").read())

Features:

• File upload or path input
• Auto-download BRDC navigation files
• Interactive Plotly charts
• Quality radar, skyplots, heatmaps, timeseries
• HTML report export

Data Sources

The library supports navigation data from:

• BRDC: IGS combined broadcast ephemeris
• SP3: Precise orbits from ESA, GFZ, CODE, WHU
• TLE: CelesTrak GNSS TLE (fallback)

Requirements

• Python 3.8+
• No runtime dependencies (Rust binary)

Optional for notebooks:

• plotly - Interactive charts
• pandas - Data manipulation
• ipywidgets - Jupyter widgets

License

MIT License - see LICENSE for details.

Author

Miluta Dulea-Flueras

Contributing

Contributions welcome! Please open an issue or pull request on GitHub.

Citation

If you use this library in research, please cite:

@software{geoveil_cn0,
  author = {Dulea-Flueras, Miluta},
  title = {geoveil-cn0: GNSS CN0 Signal Quality Analysis Library},
  year = {2026},
  url = {https://github.com/miluta7/geoveil-cn0}
}