geoveil-cn0 0.3.8

GNSS CN0 Analysis Library - Interference, Jamming & Spoofing Detection

geoveil-cn0

High-performance GNSS signal quality analysis library — Rust core, Python API

Analyze RINEX observation files to compute signal quality scores, detect threats (jamming, spoofing, interference), generate per-constellation statistics, and produce skyplot data. Used in production geodetic monitoring, precision agriculture, and GNSS security research.

---

Installation

pip install geoveil-cn0

No Rust toolchain required — pre-built wheels for Linux (x86_64 + ARM/piwheels), Windows, and macOS.

---

Quick Start

import geoveil_cn0 as gcn0

print(gcn0.VERSION)  # e.g. "0.3.7"

# Configure analysis
config = gcn0.AnalysisConfig(
    min_elevation=5.0,            # Elevation mask in degrees
    time_bin=60,                  # Time-binning interval for statistics (seconds)
    systems=["G", "R", "E", "C"], # GPS, GLONASS, Galileo, BeiDou
    detect_anomalies=True,
    anomaly_sensitivity=0.3,      # 0.1 = very sensitive, 1.0 = major events only
    interference_threshold_db=8.0,
)

analyzer = gcn0.CN0Analyzer(config)

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

# — or without navigation (CN0 analysis only) —
result = analyzer.analyze_file("observation.rnx")

# Core metrics
print(f"Quality: {result.quality_score.overall:.1f}/100 ({result.quality_score.rating})")
print(f"Mean CN0: {result.avg_cn0:.1f} dB-Hz  std: {result.cn0_std_dev:.1f}")
print(f"Jamming: {result.jamming_detected}  Spoofing: {result.spoofing_detected}")
print(f"Anomalies: {result.anomaly_count}")

# Per-constellation breakdown
for sys in result.get_systems():
    s = result.get_constellation_summary(sys)
    print(f"  {sys}: {s['satellites_observed']} sats, CN0={s['cn0_mean']:.1f} dB-Hz, avail={s['availability_ratio']}")

# Export full result
json_str = result.to_json()

---

Supported File Formats

Type	Extensions	Versions
Observation	.obs, .rnx, .crx, .YYo (e.g. .26o)	RINEX 2.x / 3.x / 4.x
Navigation	.nav, .rnx, .YYn, .YYg, .YYp	BRDC mixed / GPS / GLONASS
Precise orbits	.sp3, .SP3	SP3-c, SP3-d
Compressed	.gz, .Z, .crx	Gzip, Unix-compress, Hatanaka

---

Quality Score

Composite 0–100 score with letter rating (A–F), weighted from five components:

Component	Weight	Basis
CN0 Quality	35%	Signal strength vs. constellation thresholds
Availability	20%	Observed vs. expected satellite count
Continuity	20%	Data-gap and cycle-slip rate
Stability	15%	CN0 variance over observation window
Diversity	10%	Multi-constellation coverage

---

Threat Detection

Algorithms derived from ITU-R M.1902-1, Stanford GPS Lab, and GPS Solutions journal:

Threat	Detection Method	Default Threshold
Jamming	Rapid CN0 drop rate	>6 dB in <3 seconds
Spoofing	CN0 uniformity anomaly	std <2 dB with elevated mean
Interference	Sustained CN0 degradation	>4 dB from baseline (ITU I/N=−6 dB)

All thresholds are configurable via AnalysisConfig.

---

API Reference

AnalysisConfig

config = gcn0.AnalysisConfig(
    min_elevation=5.0,              # Elevation cutoff (degrees)
    time_bin=60,                    # Statistics time bin (seconds)
    systems=["G", "R", "E", "C"],   # G=GPS R=GLONASS E=Galileo C=BeiDou J=QZSS I=NavIC
    detect_anomalies=True,
    anomaly_sensitivity=0.3,        # 0.1–1.0
    interference_threshold_db=8.0,  # CN0 drop threshold (dB)
    verbose=False,
)

CN0Analyzer

Method	Parameters	Returns
analyze_file(obs_path)	RINEX obs path	AnalysisResult
analyze_with_nav(obs_path, nav_path)	obs + nav/SP3 path	AnalysisResult

AnalysisResult — Properties

Property	Type	Unit	Description
quality_score	QualityScore	—	Composite quality object
avg_cn0 / mean_cn0	float	dB-Hz	Mean carrier-to-noise ratio
cn0_std_dev	float	dB-Hz	CN0 standard deviation
min_cn0, max_cn0	float	dB-Hz	CN0 range
jamming_detected	bool	—	Jamming indicator
spoofing_detected	bool	—	Spoofing indicator
interference_detected	bool	—	Interference indicator
anomaly_count	int	—	Total anomaly events
duration_hours	float	h	Observation window length
epoch_count	int	—	Number of epochs parsed
constellations	list[str]	—	Constellation names present
rinex_version	str	—	File format version
station_name	str	—	Marker name from RINEX header

AnalysisResult — Methods

result.get_systems()                      # -> List[str]   constellation codes
result.get_constellation_summary("GPS")   # -> Dict        per-constellation stats
result.get_anomalies()                    # -> List[Dict]  anomaly event list
result.get_timeseries_data()              # -> Dict        time-binned CN0 series
result.get_timestamps()                   # -> List[str]   ISO timestamp strings
result.get_mean_cn0_series()              # -> List[float]
result.get_satellite_count_series()       # -> List[int]
result.get_skyplot_data()                 # -> List[Dict]  satellite az/el tracks (needs nav)
result.to_json()                          # -> str         full JSON export

QualityScore

qs = result.quality_score
qs.overall        # float  0–100
qs.rating         # str    "A (Excellent)" … "F (Very Poor)"
qs.cn0_quality    # float  component score
qs.availability   # float  component score
qs.continuity     # float  component score
qs.stability      # float  component score
qs.diversity      # float  component score

---

Jupyter Notebook Widget

An interactive analysis widget is included (notebooks/geoveil-cn0-widget.ipynb):

pip install geoveil-cn0 plotly pandas ipywidgets matplotlib
jupyter notebook notebooks/geoveil-cn0-widget.ipynb

Features:

• RINEX file upload or path input
• BRDC navigation auto-download from IGS/MGEX
• Interactive Plotly charts: CN0 timeseries, skyplot, signal heatmap, quality radar
• Per-constellation signal statistics
• Anomaly event list
• One-click HTML report export

---

Batch Processing

For large-scale production use, geoveil-cn0-batch wraps this library in a scalable microservices stack:

• FastAPI REST + WebSocket API
• Celery + Redis distributed task queue
• MongoDB result storage
• MinIO RINEX and graph data storage
• React web dashboard with live progress, skyplots, and export

Supports hundreds of RINEX files per session with per-session analysis settings, automatic ephemeris download, and multi-worker horizontal scaling.

---

Requirements

• Python 3.9+
• No runtime dependencies (pure Rust binary)

Optional (notebooks):

• plotly, pandas, ipywidgets, matplotlib

---

License

MIT — see LICENSE.

Author

Miluta Dulea-Flueras — [e-mail]

Contributing

Issues and pull requests welcome at github.com/miluta7/geoveil-cn0.

Citation

@software{geoveil_cn0,
  author    = {Dulea-Flueras, Miluta},
  title     = {geoveil-cn0: High-Performance GNSS CN0 Signal Quality Analysis Library},
  year      = {2026},
  version   = {0.3.7},
  url       = {https://github.com/miluta7/geoveil-cn0},
  license   = {MIT}
}