_{Real geoveil-cn0 output · BOR1 (EPN) · 31 December 2025 · interactive version}

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

_{Quality score and components from analyze_with_nav · BUCU (Bucharest) · 1 h at 1 Hz}

_{Mean CN0 by elevation per constellation · BOR1 · 24 h at 30 s}

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 AnalysisConfig, CN0Analyzer

config = AnalysisConfig(
    min_elevation=10.0,          # mask angle, degrees
    time_bin_seconds=60,         # 1-minute bins
    anomaly_sensitivity=0.5,     # 0.0 = permissive, 1.0 = strict
    interference_threshold_db=6.0,
)

analyzer = CN0Analyzer(config)
result = analyzer.analyze_file("COST00ROU_R_20260980000_01D_30S_MO.rnx")

q = result.quality_score
print(f"Quality score : {q.overall:.1f} / 100  ({q.rating})")
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")
print(f"Constellations: {', '.join(result.get_systems())}")

geoveil-cn0 reads plain RINEX. Decompress Hatanaka (.crx, .??d) and gzip files first, for example with the hatanaka package: hatanaka.decompress_on_disk("file.crx.gz").

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:.0f}%")
    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 / .??o"] --> 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

Public IGS files from 31 December 2025, plain RINEX, one core of an Intel Xeon E5-2620 v3 (2014), median of three runs, min_elevation=10, 60 s bins:

The analysis is single-threaded; CN0Analyzer is stateless, so a ThreadPoolExecutor over files scales with the cores available. analyze_with_nav adds visibility prediction from broadcast ephemeris.

Quality Score Components

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

Rating: A ≥ 90 · B ≥ 80 · C ≥ 70 · D ≥ 60 · F < 60. result.score and result.quality_grade report the same score.

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

AnalysisConfig

CN0Analyzer

AnalysisResult — key members

Supported Formats

Compressed files (Hatanaka .crx/.??d, gzip, ZIP) must be decompressed first.

Website

miluta7.github.io/geoveil-cn0: live CN0 skyplot, per-constellation signal curves and benchmarks from real geoveil-cn0 output.

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.

License

PolyForm Noncommercial 1.0.0 with an attribution term — see LICENSE.

Free for research, education, personal and other non-commercial use, provided you credit the author and cite the library (see Citation below). Commercial use requires a separate license — contact miluta.flueras@cartografie.ro.

Citation

@software{geoveil_cn0_2026,
  title   = {geoveil-cn0: High-performance GNSS signal quality analysis},
  author  = {Dulea-Flueras, Miluta},
  year    = {2026},
  version = {0.4.2},
  url     = {https://github.com/miluta7/geoveil-cn0},
}