Concepts — a plain-language primer

This page explains what Kshana does and why, starting from zero and building up to the physics. No prior background is assumed for the first part; the later sections add the precise relations for specialists. For word-by-word definitions see the Glossary.

1. The problem, in one paragraph#

Almost everything that needs to know where it is or what time it is — phones, aircraft, ships, satellites, power grids, financial systems — leans on signals from navigation satellites (GPS (Global Positioning System) and its siblings, collectively GNSS (global navigation satellite system)). Those signals are weak and easily lost: jammed, blocked, or simply out of view in space. When that happens, a system has to keep going on its own using onboard sensors — a clock to hold time, and inertial sensors to track motion. The question Kshana answers is simple to state and hard to measure: how long, and how well, can it keep going?

2. Why "quantum"?#

Onboard sensors drift. A clock slowly loses time; an inertial sensor slowly loses track of position. Quantum clocks and inertial sensors drift far more slowly than the classical parts in use today — so a vehicle could coast through a much longer GNSS outage while staying within its accuracy limits. That advantage is the entire promise of quantum PNT (positioning, navigation and timing). But "far more slowly" needs to be turned into numbers: how many extra minutes of holdover? how many fewer metres of drift? Those numbers decide whether a quantum payload is worth its cost, mass, and power.

3. What Kshana actually does#

Kshana is a simulator, not hardware and not a hardware design. It:

1. Takes a scenario — a timeline with a stretch of GNSS outage, and the published performance figures of a sensor.
2. Drives a sensor error model through that timeline: while GNSS is available the model is disciplined to the truth; during the outage it free-runs, accumulating error exactly as the physics says it should.
3. Scores the result against operational figures of merit (how big the error gets, how long it stays in spec, how trustworthy the estimate is).
4. For the sensor packs, does this twice — once for a quantum sensor, once for its classical counterpart — on the same scenario, so the comparison is apples-to-apples.

Crucially, the engine knows nothing about "quantum" vs "classical". Both are just error models with different (published, cited) parameters. The difference you see in the output is the difference in the published physics — nothing is hand-tuned to favour one side.

4. The four building blocks ("packs")#

The hybrid pack is the punchline: a navigation solution needs both good time and good position. It shows that an optical timing link can keep even a modest clock locked — which means the inertial sensor becomes the weakest link, and that is exactly where a quantum accelerometer pays off.

Beyond the four packs#

The same engine runs many more scenario kinds; kshana kinds lists them all. They reach past the sensors to the signals and the geometry the sensors depend on:

• Where the satellites are — orbit propagation, constellation design around the Earth, the Moon or another body, and the positions of the solar-system bodies.
• What the receiver hears — the L-band spectrum under a jammer, jamming and spoofing, and tracking-loop loss of lock.
• Whether the answer can be trusted — integrity monitoring (RAIM, receiver autonomous integrity monitoring, and its advanced form ARAIM) with protection levels.
• Alternatives to GNSS — gravity and terrain map matching, and positioning, navigation and timing from low Earth orbit (LEO) satellites, from the signal design through the link budget and the navigation message to the position fix.
• Timing for networks — telecom holdover checked against the international masks, and slot timing for time-indexed schedules.
• Beyond Earth — lunar time and reference frames, lunar service volumes and Mars relay navigation.
• Many runs at once — campaigns chain kinds on one timeline, sweep a parameter or run a Monte Carlo ensemble.

5. Honesty by construction#

A simulator is only useful if you can trust it. Kshana is built so that you can:

• Every parameter is cited. Each sensor figure carries a provenance string naming the datasheet or paper it came from. No anonymous constants.
• Every model is checked against a textbook relation, not just against itself — e.g. the simulated clock's Allan deviation must match the published stability figure, and the inertial drift must match the standard error-growth law. A capability is called validated only where an independent external oracle (a real dataset, an independent implementation or published reference vectors) agrees; the rest is modelled, and the verification matrix says which is which.
• Maturity is labelled. VALIDATION.md marks each effect validated, modelled or not modeled, and states plainly that the optical-clock figures are laboratory / space-goal numbers — no strontium optical clock has flown.
• Results are reproducible to the bit: the same scenario, seed, and version always produce the identical answer on the same platform; across operating systems the figures agree to 1e-6 relative (see REPRODUCIBILITY.md).

6. The physics, for specialists#

The relations Kshana implements and tests (full detail and tolerances in VALIDATION.md):

• Clock holdover. Two-state phase/frequency model with white frequency modulation (white FM, power spectral density q_wf), random-walk FM (q_rw), flicker FM (a sum of log-spaced Ornstein–Uhlenbeck processes calibrated to a flat Allan floor), and deterministic aging. Validated by overlapping Allan deviation against the published σ_y(τ) (Riley, NIST (National Institute of Standards and Technology) SP (Special Publication) 1065).
• Inertial dead-reckoning. Residual accelerometer bias → ½·b·T²; velocity random walk → σ_x(T) = √(S_a·T³/3); optional gyro bias and angular random walk produce a tilt error that couples gravity (g·θ) into horizontal acceleration (Groves).
• Time transfer. White timing jitter → synchronisation precision → one-way ranging (range = c·dt, 1 ps ≈ 0.3 mm); the sample mean averages as σ/√N.
• Fusion & integrity. A two-state Kalman filter whose process noise matches the truth model; coasting, its phase-error variance grows to exactly q_wf·T + q_rw·T³/3 — the analytic holdover relation — and its 1-σ bound feeds the Integrity figure of merit.
• Geometry. Keplerian two-body propagation (optionally with secular J2) or SGP4 (Simplified General Perturbations 4) from real two-line element sets, Walker constellations, and line-of-sight visibility (Earth occultation + elevation mask) derive GNSS availability from orbital geometry rather than hand-authored windows.

7. Where to go next#

• Work through it: the tutorials — three worked examples + graded exercises.
• Run it: see the README quick start.
• Understand the structure: ARCHITECTURE.md.
• Check what is and isn't validated: VALIDATION.md.
• Look up a term: GLOSSARY.md.