PNT-resilience framework cross-walk

Aligned to, not certified. This maps Kshana's simulated capabilities to the published PNT-resilience (PNT: positioning, navigation and timing) frameworks. It is a simulation-derived self-assessment aligned to the DHS/CISA (DHS: Department of Homeland Security; CISA: Cybersecurity and Infrastructure Security Agency) Resilient PNT Conformance Framework (RPCF) v2.0, the RethinkPNT/Firesmith Resist-Detect-Respond-Recover (RDRR) model, and Yang Yuanxi's resilient-PNT criteria. It is not a certification, accreditation, or compliance statement, and it carries no endorsement from DHS, IEEE (Institute of Electrical and Electronics Engineers), or any authority. Every row is labelled with its honest verification status (VALIDATED against an external oracle, or MODELLED from first principles with tests); the resilience-scoring layer itself is MODELLED. Two of its kernels carry their own VALIDATED rows in the verification matrix, the inverse-Simpson diversity kernel (resilience::diversity) and the rank-order statistics kernel (resilience::stats); that validates the arithmetic, not the scores built on it.

The scoring engine (src/resilience/) consumes these capabilities and emits per-dimension sub-scores, never a single phantom number. See src/verification.rs for the machine-checked status invariants.

DHS RPCF v2.0 technique categories (the seven; "OLVIDMR", from their initials, is our mnemonic)#

Category	Kshana capability used as the sub-score driver	Module	Status
Obfuscate	Declared technique × source quality (no direct behavioural driver)	`resilience::score`	MODELLED
Limit	Declared technique × source quality	`resilience::score`	MODELLED
Verify	Impairment-detector AUC (area under the curve) under the scenario (spoof/jam/meacon monitors)	`impairment_eval`, `resilience::score`	MODELLED
Isolate	Declared technique × source quality	`resilience::score`	MODELLED
Diversify	Independent-group count → inverse-Simpson effective diversity	`resilience::diversity`	MODELLED
Mitigate	Availability under denial	`fom`, `resilience::score`	MODELLED
Recover	Holdover coast × bounded-degradation gate	`fom`, `holdover`, `resilience::timeline`	MODELLED

RethinkPNT / Firesmith RDRR functions#

Function	Driver	Module	Status
Resist	Mean of Obfuscate/Limit/Isolate/Diversify sub-scores	`resilience::score`	MODELLED
Detect	Verify sub-score (detector AUC)	`resilience::score`	MODELLED
Respond	Mitigate sub-score (availability)	`resilience::score`	MODELLED
Recover	Holdover × bounded gate; resilience-timeline key performance indicators (KPIs: detect/react/recover/loss)	`resilience::timeline`	MODELLED

Yang Yuanxi criteria (the subset the timing/detection figures of merit speak to)#

Criterion	Driver	Module	Status
Availability	Availability under denial	`resilience::score`	MODELLED
Reliability	Filter integrity fraction	`fom`, `resilience::score`	MODELLED
Continuity	Holdover × bounded gate	`resilience::timeline`	MODELLED
Accuracy	Timing/quality proxy — position-domain accuracy is not part of the score. `fom::positioning_performance` turns a supplied position covariance and HPL (horizontal protection level) into CEP/SEP/2DRMS (circular error probable, spherical error probable, twice the distance root mean square), but no scenario feeds it into the resilience score	`resilience::score`	MODELLED (timing-domain only)

What this cross-walk does not claim#

No certified RPCF Level. The assigned Level is a tentative, simulation-derived reading with an explicit bounded-degradation gate, not a conferred maturity.
No position-domain accuracy in the score. The resilience sub-scores are built from timing-domain and detection metrics. The engine does compute positions elsewhere (the pvt, gnss-ins and leo-pvt kinds) and protection levels (the integrity kind), but the scoring layer does not consume them.
No field validation. The reference panel and threat ensemble are synthetic, parameter-grounded reductions (resilience::panel), stated as a first-class limitation.

The companion study (resilience::study) exists precisely to show that collapsing these dimensions into one composite score or one Level produces a rating whose architecture ranking is unstable under defensible weighting and threat choices — so this cross-walk is a measurement layer to be read per-dimension, not a single grade to certify against.

Resilience and vertical scenarios that exercise these layers#

The bundled scenarios below run the threat and recovery machinery the cross-walk rows point at. Each is a real run of the named kind; every one is MODELLED (no field data).

Scenario (`scenarios/`)	Kind	What it shows
`conflict-resilience.toml`	`conflict-resilience`	Layered PNT under a shared jamming/spoofing threat: a seeded Monte Carlo of layer denial, inverse-variance fusion of the survivors, the single-layer vs layered total-loss ratio, and how correlated denial collapses that ratio. Its Monte-Carlo-to-closed-form and fusion identities are checked by in-crate tests; the kind has no verification-matrix row, so it is graded MODELLED here.
`leo-resilience-gnss-jammed-leo-carries.toml`	`campaign`	GNSS (global navigation satellite system) jammed, a low Earth orbit (LEO) PNT layer carries the user, integrity kept (three phases on one timeline).
`leo-resilience-multiband-diversity.toml`, `leo-resilience-spoof-monitors.toml`	`campaign`	One jammer against a four-band receiver; which spoofing monitor sees which spoofer.
`leo-resilience-js-margin.toml`	`spectrum`	The jammer-to-signal (J/S) margin from received power.
`leo-resilience-spoof-doppler.toml`	`leo-pass`	Spoofing detection by Doppler and pass-geometry consistency.
`leo-vertical-5g-network-timing.toml`, `leo-vertical-critical-infrastructure-timing.toml`	`campaign`	Base-station and critical-infrastructure timing in holdover, with LEO time.
`leo-vertical-autonomous-vehicle.toml`, `leo-vertical-rail-maritime.toml`, `leo-vertical-polar-arctic.toml`, `leo-vertical-asset-tracking-iot.toml`	`campaign`	Road, rail, maritime, polar and Internet of Things (IoT) users with a LEO layer.
`automotive-urban-canyon.toml`, `small-uas-jammed-nav.toml`	`gnss-ins`	GNSS/INS (inertial navigation system) dead reckoning through an urban canyon and through jamming on a small uncrewed aircraft.
`maritime-port-approach-coast.toml`, `rail-tunnel-coast.toml`	`ins-trn-coast`	How long an INS keeps a ship or a train inside its limit with GNSS denied.
`maritime-strait-jamming.toml`, `maritime-spoof-position-push.toml`	`jamming`, `spoof-detect`	A maritime jammer; detecting a spoofer that drags a ship's reported position.