geoveil-mp

GNSS code multipath analysis — Rust core, Python API

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Code multipath RMS by signal — real 24 h / 30 s multi-GNSS data

Anubis/TEQC-style code multipath estimation for every pseudorange observable in a RINEX file — per-code MP linear combinations, arc-wise ambiguity debiasing, interval-aware cycle-slip detection, and SNR series export. Built for continuous station monitoring on long time series of 1 s or 30 s data, following the methodology of Hunegnaw & Teferle (Sensors 2022).

Part of the GeoVeil suite together with geoveil-cn0 (CN0 signal quality and threat detection).

Installation

pip install geoveil-mp

Pre-built wheels for Linux, Windows, and macOS — no Rust toolchain required.

# Rust
[dependencies]
geoveil_mp = "0.2"

Quick Start

import geoveil_mp as gm

obs = gm.read_rinex_obs("BOR100POL_R_20240010000_01D_30S_MO.rnx")
print(obs.num_epochs, obs.num_satellites, obs.interval)   # 2880 104 30.0

analyzer = gm.MultipathAnalyzer(obs, elevation_cutoff=10.0, systems=["G", "R", "E", "C"])
results = analyzer.analyze()

# One statistics row per signal code: GPSM1C, GPSM2W, GALM8X, GLOM1P, BDSM2I, ...
for s in sorted(results.statistics, key=lambda s: s.signal):
    print(f"{s.signal}  rms={s.rms:.3f} m  n={s.count}  slips={s.cycle_slips}")

# Cycle slips, counted per carrier-phase signal
print(results.cycle_slip_counts)          # {'GPSL1C': 210, 'GPSL2W': 168, ...}

# Attach precise elevations from SP3 (recomputes elevation-weighted RMS)
sp3 = gm.read_sp3("COD0MGXFIN_20240010000_01D_05M_ORB.SP3")
computed, failed = results.compute_elevations(sp3, obs.approx_position)

# SNR time series per satellite and S-code (for SNR-residual multipath analysis)
for series in obs.get_snr_series("G07"):
    print(series.code, len(series), series.values[:3])
Debiased MP series for one satellite — the oscillation is site multipath

What it computes

Per-code multipath combinations

For every pseudorange code observable P_k (C1C, C1X, C2W, C2X, C5X, ...), the ionosphere-free, geometry-free multipath combination is formed with two carrier phases:

MP_k = P_k − (1 + 2/(α−1))·Φ_i + (2/(α−1))·Φ_j        α = (f_i / f_j)²
  • Φ_i — phase on the code's own band; Φ_j — phase on a partner band chosen from a deterministic per-system priority list
  • GLONASS FDMA frequencies use the per-satellite channel numbers from the RINEX header
  • The phase-ambiguity bias is removed per continuous arc: whole-arc mean for short arcs, centered moving average (default 1500 s) for long ones
  • Arcs reset at data gaps, cycle slips, and MP jumps — slips never smear into the RMS

Signals are named in the convention used by GNSS monitoring literature: GPSM1C, GPSM2W, GLOM1P, GALM8X, BDSM2I.

Cycle-slip detection

Three detectors, thresholds that scale with the sampling interval (|Δ| > base + rate·dt):

Method Test Default threshold
Geometry-free ΔGF per phase code vs. reference band 0.10 m + 0.003·dt
Code-phase Δ(Φ − P) per code 5.0 m + 0.10·dt
LLI RINEX loss-of-lock flags (phase only) —

At 30 s sampling this detects single-cycle L1 slips (GF signature ≈ 0.29 m vs. threshold 0.19 m) — rate-based thresholds tuned for 1 s data cannot. Slips are attributed and counted per signal code (GPSL2W, GLOL1C, ...).

SNR series export

RinexObsData.get_snr_series(satellite, code=None) returns per-S-code time series (unix-second timestamps, dB-Hz values) — the input for SNR-residual multipath analysis (polynomial detrending, wavelet spectra) without re-parsing the file.

Results on real data

24 h of BOR1 (EPN, Poland) 30 s multi-GNSS RINEX 3, default settings:

Constellation Best signal RMS Worst signal RMS
Galileo E5 AltBOC (M8X) 0.165 m E5b (M7X) 0.395 m
GPS L2C (M2X) 0.312 m L1 C/A (M1C) 0.342 m
BeiDou B2I (M7I) 0.295 m B3I (M6I) 0.354 m
GLONASS L2 P (M2P) 0.318 m L1 C/A (M1C) 0.566 m

The ranking (Galileo AltBOC best, GLONASS C/A worst) reproduces published station-monitoring results. Parsing the file takes ~0.4 s; the full 15-signal analysis with slip detection ~1.4 s (306 k estimates).

Analyzer options

gm.MultipathAnalyzer(
    obs,
    elevation_cutoff=10.0,        # degrees; applied when elevations are known
    systems=["G", "R", "E", "C"], # G R E C J S I
    bias_window_seconds=1500.0,   # moving-average window; None = whole-arc mean
    min_arc_seconds=300.0,        # drop shorter arcs
    arc_gap_factor=5.0,           # arc break at gap > factor × interval
    include_codes=["C1C", "GC5X"],# restrict codes ("C1C" any system, "GC5X" GPS only)
    exclude_codes=[],
    max_epochs=None,              # uniform decimation guard for huge files
    detect_cycle_slips=True,
    ion_delta_base=0.10, ion_delta_rate=0.003,   # GF slip threshold (m, m/s)
    cp_delta_base=5.0,   cp_delta_rate=0.10,     # code-phase slip threshold
)

API surface

Object Key members
RinexObsData num_epochs, num_satellites, interval, marker_name, approx_position, satellites(), observation_types(sys), glonass_fcn(), snr_codes(sat), get_snr_series(sat, code=None)
MultipathAnalyzer analyze() → AnalysisResults
AnalysisResults estimates, statistics, cycle_slips, cycle_slip_counts, total_estimates(), total_cycle_slips(), compute_elevations(sp3, receiver)
MultipathEstimate satellite, system, signal ("C1C"), epoch, mp_value, elevation, azimuth, snr
MultipathStats signal ("GPSM1C"), system, code, count, rms, weighted_rms, mean, std_dev, min, max, cycle_slips
CycleSlip satellite, epoch, signal, system, magnitude, threshold, method ("gf", "code_phase", "lli")
SnrSeries satellite, system, code, times, values, epochs_iso()
Sp3Data satellites(), get_position(sat, epoch), num_epochs, interval
Functions read_rinex_obs, read_rinex_obs_bytes, read_sp3, calculate_azel, compute_elevation, get_frequency, get_wavelength, version

Supported input: RINEX v2 / v3 / v4 observation files, SP3-c/d orbits, broadcast ephemerides (Keplerian + GLONASS RK4).

Pipeline

flowchart LR
    A[RINEX v2/v3/v4] --> B[Parse<br/>code + phase + SNR]
    B --> C[Per-code combos<br/>deterministic phase pairing]
    B --> D[Cycle-slip detection<br/>GF · code-phase · LLI]
    D --> E[Arc splitting]
    C --> E
    E --> F[Ambiguity debiasing<br/>mean / moving average]
    F --> G[Per-signal statistics<br/>RMS · weighted RMS]
    H[SP3 orbits] -.-> I[Elevations / azimuths]
    I -.-> G

CLI

geoveil-mp analyze --obs station.rnx --sp3 orbits.sp3 --elevation 10 --output results/
geoveil-mp info --obs station.rnx

References

  • Hunegnaw, A.; Teferle, F.N. Evaluation of the Multipath Environment Using Electromagnetic-Absorbing Materials at Continuous GNSS Stations. Sensors 2022, 22, 3384.
  • Estey, L.H.; Meertens, C.M. TEQC: The Multi-Purpose Toolkit for GPS/GLONASS Data. GPS Solutions 1999, 3, 42–49.
  • Václavovic, P.; Douša, J. G-Nut/Anubis: Open-Source Tool for Multi-GNSS Data Monitoring. IAG Symposia 2016, 143.

License

MIT — see LICENSE.

Author

Miluta Dulea-Flueras — miluta.flueras@cartografie.ro

Citation

@software{geoveil_mp,
  author  = {Dulea-Flueras, Miluta},
  title   = {geoveil-mp: GNSS Code Multipath Analysis Library},
  year    = {2026},
  version = {0.2.0},
  url     = {https://github.com/miluta7/geoveil-mp},
  license = {MIT}
}

Project links

Data verified by PyPI on Jul 2, 2026
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  • Released:
Newer release available (0.2.1)

1 maintainer

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Avatar for miluta from gravatar.com miluta

Credits

Author/Maintainer: Miluta-Dulea Flueras

GitHub Statistics

Data verified by PyPI on Jul 2, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.8

Provides Extra

all dev notebook

Tags

gnss gps rinex multipath galileo glonass beidou satellite navigation positioning sp3 ephemeris

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Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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