Run the validated Kshana PNT-resilience simulator (orbits, frames, GNSS, fusion) from an AI agent.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
Not yet automatically verified
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.
Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Documentation Overview
क्षण — Sanskrit for the precise instant, the smallest measure of time.
Open, reproducible PNT (positioning, navigation, and timing) resilience simulation with published quantum-sensor performance models.
Kshana (क्षण, Sanskrit: "the precise instant") is an open, reproducible PNT-resilience simulator with quantum-sensor performance models — positioning, navigation, and timing. It compares quantum and classical sensors mostly from published Allan/noise-budget coefficients, with a first-principles cold-atom- interferometer accelerometer layer (Mach–Zehnder phase, quantum projection noise, contrast decay, and vibration coupling) that derives the noise coefficient rather than looking it up; it is not yet a full quantum-physics simulator (Coriolis and light-shift systematics remain coefficient-level — see docs/QUANTUM.md and docs/QUANTUM-MODELS.md).
It quantifies, in hard and reproducible numbers, what quantum clocks, quantum
inertial sensors, and optical time-transfer buy a navigation system over classical
PNT — scored against the operational figures of merit that matter for resilient
navigation. Every result is reproducible from scenario + seed + engine version,
and every sensor parameter is traceable to a published source — consolidated in one
citable table in docs/PROVENANCE.md.
Validated, not asserted. 666/666 AIAA SGP4 vectors to 4.12 mm · Cowell force model 0.08 m vs Orekit 12.2 · Galileo 0.61 m / Swarm-A 0.10 m vs real ESA precise ephemerides · GCRS→ITRS bit-for-bit vs SOFA/ERFA · ML metrics exact vs scikit-learn · 64 of 168 capabilities validated against independent external oracles; 100 honestly labelled Modelled.
One engine, five front doors · SVG
Validated against external oracles — every row CI-gated
Each row is checked against an independent external oracle (real dataset, independent reference implementation, or published reference vectors) and re-checked in CI (continuous integration). Full 168-row matrix →
| Capability | Result | External oracle | |
|---|---|---|---|
| ✅ | SGP4/SDP4 (Simplified General Perturbations 4 / Simplified Deep-space Perturbations 4, the standard analytic satellite-orbit propagators) propagation | 666/666 vectors, worst 4.12 mm | AIAA 2006-6753 (Vallado; AIAA = American Institute of Aeronautics and Astronautics) + independent sgp4 crate |
| ✅ | Numerical Cowell force model | 0.08 m / 24 h, 275 epochs | Orekit 12.2 DormandPrince853 (CS GROUP) |
| ✅ | Orbit fit vs precise ephemeris | Galileo 0.61 m · Swarm-A 0.10 m | ESA (European Space Agency) / ESOC (European Space Operations Centre) SP3 (Standard Product 3, the precise-orbit format of the IGS — the International GNSS (global navigation satellite system) Service) precise orbits |
| ✅ | GCRS→ITRS (Geocentric Celestial Reference System → International Terrestrial Reference System) frame chain | bit-for-bit vs SOFA (the International Astronomical Union's Standards of Fundamental Astronomy library); ≤ 0.86 m vs SPICE (Spacecraft, Planet, Instrument, C-matrix, Events — the planetary-geometry toolkit) | ERFA (Essential Routines for Fundamental Astronomy, the open port of SOFA)/SOFA + ANISE (Attitude, Navigation, Instrument, Spacecraft, Ephemeris — a pure-Rust SPICE) |
| ✅ | Allan deviations | reproduce reference deviations | NIST SP 1065 (National Institute of Standards and Technology Special Publication 1065) + Stable32 on a real Cs (caesium) clock |
| ✅ | Global navigation satellite system (GNSS) dilution of precision (DOP) · machine-learning (ML) detector metrics | to 1e-6 · to 1e-9 | gnss_lib_py · scikit-learn |
| ✅ | Fisher information · CRLB (Cramér–Rao lower bound) · observability | eigh / CRLB / DOP to 1e-9 | NumPy 2.4.1 (LAPACK, the Linear Algebra PACKage) + Kay (1993) closed forms |
64 Validated · 100 Modelled · 4 Partner — SVG
Free and open source under the GNU AGPL-3.0 (GNU Affero General Public License, version 3)
— with a commercial licence available from Ashforde OÜ (an Estonian private limited
company; OÜ = osaühing) for proprietary/closed integration (see LICENSING.md).
Professionally developed and maintained by Ashforde OÜ; commercial
support, integration, and proprietary extensions available.
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
6
Stargazers on the source repository.
Last commit
23d ago
Most recent push to the default branch.
Reviews
No reviews yet — be the first to share how this listing worked for you.
Frequently Asked Questions about Kshana PNT Resilience simulator
AllMCPs Directory Badge
Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.
Badge Style:
Live Dynamic SVG Preview
Markdown (GitHub README)
[](https://allmcps.com/mcp/kshana-pnt-resilience-simulator)HTML Embed
<a href="https://allmcps.com/mcp/kshana-pnt-resilience-simulator"><img src="https://allmcps.com/api/badge/kshana-pnt-resilience-simulator?style=directory" alt="Kshana PNT Resilience simulator on AllMCPs" /></a>