Scenario kind pagesEdit on GitHubSource: docs/kinds/by-kind/orbit.md

orbit (scenario kind)

Scenario kind of the engine v0.38.0, family Constellations, coverage and geometry. Advisory: analysis output from a research and engineering tool, not type-approved navigation equipment and not a safety-of-life service. Every label here is the engine's or the verification matrix's; a kind is MODELLED unless the matrix says otherwise (how a label is earned: the README's Evidence section).

What it does#

GNSS availability + DOP from an orbital constellation (Walker / TLE / RINEX).

One run of scenarios/orbit-rinex.toml summarises as: scenario bffc887db977 | 77/361 samples GNSS-nominal | best PDOP 2.53 pos 2.53m | quantum holdover 0s p95 6.49e-5ns integrity 1.000 security n/a (no attack) | classical holdover 0s p95 25.0ns integrity 1.000 security n/a (no attack)

Inputs#

From the schema (kshana kinds --json):

  • Required fields: seed, threshold_ns, mask_deg, time, user|user_site, constellation, clock_quantum, clock_classical
  • Optional fields: sigma_uere_m, constellations, epoch, export_sp3, export_omm, export_oem, skyline

The example scenarios/orbit-rinex.toml sets, among others:

Field Value Unit
clock_classical.id csac-sa45s text
clock_classical.provenance Microchip SA.45s / SA65 chip-scale atomic clock, sigma_y(1s) text
clock_classical.q_rw 1.0000e-28 1/s
clock_classical.q_wf 9.0000e-20 s^2/s
clock_classical.y0 1.0000e-11 1
clock_quantum.id optical-sr-lattice text
clock_quantum.provenance Strontium optical lattice clock, space-oriented goal sigma_y text
clock_quantum.q_rw 1.0000e-40 1/s
clock_quantum.q_wf 1.0000e-30 s^2/s
clock_quantum.y0 1.0000e-15 1
constellation.rinex 3.04 N: GNSS NAV DATA G: GPS text
kind orbit text

Run it#

kshana scenarios/orbit-rinex.toml

7 bundled scenarios use this kind; the others are in scenarios/.

What it gives#

The scalar results of that run, with the units the engine states:

Result Value Unit
classical.filter_health.consistent true flag
classical.filter_health.nees_chi2_lower_95 1.540167 1
classical.filter_health.nees_chi2_upper_95 2.518928 1
classical.filter_health.nees_mean 1.963599 1
classical.filter_health.nis_chi2_lower_95 0.975649 1
classical.filter_health.nis_chi2_upper_95 1.024647 1
classical.filter_health.nis_mean 1.000119 1
classical.fom.availability 0.645429 1
classical.fom.holdover_s 0 s
classical.fom.integrity 1 1
The chart the engine writes for scenarios/orbit-rinex.toml, kind orbit

Evidence#

MODELLED 4 · VALIDATED 4: the verification matrix rows that name this kind.

  • VALIDATED Earth rotation from a two-part UT1 Julian date on the orbit path, so the Earth-fixed position keeps the microsecond a single-f64 date loses (about 3e-9 rad near 2020): jd2::gmst82 (IAU 1982 GMST, the TEME convention, on the two parts), cio:
  • VALIDATED GDOP/PDOP/HDOP/VDOP/TDOP from line-of-sight geometry via Q=(HᵀH)⁻¹ with a local ENU split Tests: dop_reference.rs.
  • MODELLED dop_error_mc::run_geometry and check: a seeded Monte Carlo (N = 20,000 epochs per geometry, sigma = 1.5 m, ChaCha8 seed 36 + i) of the engine's own iterated least-squares fix (pvt::solve_spp, unit weights, solved from the true position) on Tests: uere_dop_monte_carlo.rs.
  • VALIDATED skyline + frames::look_angles + geometry::sky_geometry: the geodetic (WGS-84) azimuth and elevation of a satellite from a ground site, an azimuth/elevation mask table linearly interpolated in azimuth with wrap at 360 deg, the visible set el Tests: skyline_orekit_oracle.rs.
  • and 4 more in the matrix.

The rows the run itself exercised, with the oracle the matrix gives each (the public sources are named there):

  • VALIDATED Allan/modified/Hadamard deviation + power-law noise ID with χ² CIs. Oracle (ExternalDataset): NIST SP 1065 (Riley) / Stable32 reference deviations on NBS14
  • VALIDATED GDOP/PDOP/HDOP/VDOP/TDOP from line-of-sight geometry via Q=(HᵀH)⁻¹ with a local ENU split. Oracle (ExternalDataset): gnss_lib_py 1.0.4 (Stanford NAV Lab) DOP — independent library, matched to 1e-6 relative
  • MODELLED Closed-form coast-error growth + holdover-to-threshold; quantum-clock classes. Oracle (ReferenceImpl): Multi-step clock_state covariance recursion (same-codebase cross-check); the underlying coast-variance & holdover-inversion kernel is externally validated vs scipy (see 'Clock-holdover coast-variance & threshold inversion'). The per-class red-noise-floor holdover figures stay MODELLED
  • MODELLED 3-state (phase/freq/drift) van-Loan Kalman clock, Joseph-stabilised. Oracle (ReferenceImpl): filterpy 1.4.5 KalmanFilter (R. Labbe, MIT), with F via scipy.linalg.expm and Q via the Van-Loan 1978 block-matrix — an independent reference implementation reproducing kshana's full filter trajectory. Cross-implementation consistency: the clock physics / Allan calibration are not externally validated, so this stays MODELLED. 0.30 external comparison, a finding (stays MODELLED): on IGS final 30 s clocks of 11 GPS Blo
  • MODELLED Clock-aided χ², RAIM, AGC, SQM fused per-epoch security FoM. Oracle (ExternalDataset): TEXBAT scenario parameters (Humphreys 2012) — characterisation, not pinned vectors. 0.30 external comparison, a finding (stays MODELLED): on JammerTest 2024 (Bleik; GPL-3.0 data) the observable-level monitors (clock-aided chi-square, RAIM, solve failure) raised no alarm within 10 s of any of the 8 evaluable published spoofing onsets (where the pre-onset window was clean, the first alarm came 10.2 to 39 s after the pu

Limitations#

What the run's own report says is not modelled or not validated:

  • GNSS-denied clock holdover is MODELLED, not validated: checked against a separate implementation in this same codebase — independent of the unit under test, but not externally authoritative. (verification matrix (docs/MODELLED-RATIONALE.md))
  • Onboard clock state estimation is MODELLED, not validated: checked against a separate implementation in this same codebase — independent of the unit under test, but not externally authoritative. (verification matrix (docs/MODELLED-RATIONALE.md))
  • Spoofing detection is MODELLED, not validated: a sub-claim is externally checked, but the whole capability composes modelled pieces, so the capability stays Modelled. (verification matrix (docs/MODELLED-RATIONALE.md))
  • Reproducibility & software assurance is MODELLED, not validated: a sub-claim is externally checked, but the whole capability composes modelled pieces, so the capability stays Modelled. (verification matrix (docs/MODELLED-RATIONALE.md))

See also#