geoveil-cn0

High-performance GNSS signal quality analysis — 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 at Romanian national geodetic network (ROMPOS), precision agriculture, and GNSS security research.

Features

Installation

pip install geoveil-cn0

No Rust toolchain required — pre-built wheels for Linux (x86_64 + ARM/piwheels), Windows, and macOS. Python 3.9–3.12.

Quick Start

from geoveil_cn0 import CN0Analyzer, AnalyzerConfig

config = AnalyzerConfig(
    time_bin_seconds=300,        # 5-minute bins
    anomaly_sensitivity=0.5,     # 0.0 = permissive, 1.0 = strict
    interference_threshold_db=6.0,
)

analyzer = CN0Analyzer(config)
result = analyzer.analyze("COST00ROU_R_20260408_0100_30S_MO.rnx")

print(f"Quality score : {result.quality_score:.1f} / 100  ({result.quality_grade})")
print(f"Jamming       : {'⚠️  DETECTED' if result.jamming_detected else '✅ Clean'}")
print(f"Spoofing      : {'⚠️  DETECTED' if result.spoofing_detected else '✅ Clean'}")
print(f"Interference  : {'⚠️  DETECTED' if result.interference_detected else '✅ Clean'}")
print(f"Satellites    : {result.total_satellites_tracked} tracked")
print(f"Constellations: {', '.join(result.active_constellations)}")

Spoofing: visibility-based detection (new in 0.3.8)

# Requires navigation file for ephemeris comparison
result = analyzer.analyze_with_nav(
    "COST00ROU_R_20260408_0100_30S_MO.rnx",
    "BRDC00IGS_R_20260408_01D_MN.rnx",
)

if result.has_visibility_prediction:
    print(f"Confirmation rate: {result.visibility_confirmation_rate:.0%}")
    print(f"Unexpected sats  : {result.visibility_mean_unexpected:.1f}")
    print(f"Missing sats     : {result.visibility_mean_missing:.1f}")

Architecture

flowchart LR
    A["RINEX obs\n.rnx/.crx/.gz"] --> C
    B["BRDC nav\n.nav/.rnx"] --> C
    C["CN0Analyzer\nRust core"] --> D["Quality Score\n0–100"]
    C --> E["Threat Flags\nJam/Spoof/Interf"]
    C --> F["Visibility\nPrediction"]
    C --> G["Timeseries\nCN0 per bin"]
    C --> H["Skyplot\nAz/El tracks"]
    D & E & F & G & H --> I["AnalysisResult\nJSON / Python API"]

Performance

Benchmarked on a single core (Intel i7-1185G7). ThreadPool batch processing scales linearly with core count.

Quality Score Components

The composite quality score (0–100) is computed from five weighted components:

Letter grades: A ≥ 90 · B ≥ 80 · C ≥ 70 · D ≥ 60 · F < 60

Threat Detection

Spoofing detection requires a navigation file (analyze_with_nav). The 0.3.8 algorithm compares observed satellites against ephemeris predictions — a high ratio of unexplained observations indicates signal replay attacks.

API Reference

AnalyzerConfig

AnalysisResult — key properties

Supported Formats

Live Demo

batch.geoveil-rinex.eu — the GeoVeil batch dashboard runs this library in production: CN0 quality scoring, threat detection, skyplots and heatmaps for every processed RINEX file, plus advanced multipath sessions (per-code MP RMS, cycle slips, SNR-residual wavelet spectra, Fresnel zones) and long-term trend monitoring on daily 30 s station data.

Batch Processing

For large-scale processing this library is wrapped by the GeoVeil batch system (FastAPI + Celery + MongoDB + MinIO + React dashboard): parallel workers, automatic BRDC ephemeris download, per-session analysis settings, WebSocket progress, and result persistence. See the live demo above. For local scripting, CN0Analyzer is stateless — instantiate one per thread and process files with a ThreadPoolExecutor.

Citation

@software{geoveil_cn0_2026,
  title   = {geoveil-cn0: High-performance GNSS signal quality analysis},
  author  = {Dulea-Flueras, Miluta},
  year    = {2026},
  version = {0.3.8},
  url     = {https://github.com/miluta7/geoveil-cn0},
}