How Stable Is a PNT Resilience Score? Decision-Instability of Single-Number Resilience Ratings under Framework-Aligned Weighting

Jul 16, 2026

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Every authoritative positioning, navigation, and timing (PNT) resilience framework defines resilience but relies on self-attestation, offering checklists and maturity Levels without an engine for measurement, evidence, or reproducible scoring. This work builds an open deterministic scoring engine atop a PNT simulator, producing per-dimension subscores with traceable scenarios, oracle provenance, and explicit validation status. It then tests whether a single composite score or a single maturity Level is stable for decisions across weighting and threat uncertainty, and analyzes failure modes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

Introduction

Gap in PNT resilience measurement

Frameworks and lack of reproducible scoring

Composite-indicator sensitivity as the motivation

Contributions and approach

Deterministic scoring engine and subscores

Decision stability study under weighting and threats

Reporting protocol from results

How Stable Is a PNT Resilience Score? Decision-Instability of Single-Number Resilience Ratings under Framework-Aligned Weighting Chakshu Baweja Ashforde O¨U Tallinn, Estonia [contact@ashforde.org](contact@ashforde.org) arXiv :2607 .05415v1 [ cs .CR] 22 Jun 2026 Abstract—Every authoritative positioning, navigation, and timing (PNT) resilience framework, from the DHS Resilient PNT Conformance Framework (RPCF) to the Resist-Detect-RespondRecover model and Yang’s resilient-PNT criteria, defines what resilience means but supplies only self-attestation: a checklist or a maturity Level, with no engine, no measurement, and no evidence. We build the missing measurement layer as an open, deterministic scoring engine over a PNT simulator, emitting perdimension sub-scores each traceable to a scenario and an oracle and each tagged with its honest validation status. We then ask whether a single composite score, or a single maturity Level, is a stable basis for a decision. Across a reference panel of seven architectures built to span genuine cross-dimension tradeoffs, a Dirichlet weighting simplex over the seven RPCF categories, anda five-threat ensemble, the answer separates into two regimes. The composite winner is stable under active denial and under near-equal weightings, flipping in about 1 percent of draws, so a single number is safe precisely where one design dominates; but re-weighting alone flips the winner in up to 22 percent of draws under nominal conditions, where designs genuinely contend, and that instability is a domain application of known compositeindicator sensitivity. The sharper and weighting-invariant failure is categorical: a weakest-link maturity Level (our minimumover-categories operationalization of the RPCF ladder, not the framework’s own rule) is a function of the threat assumed rather than of the architecture, changing for one architecture in seven across the ensemble. A constructed example shows that, because the composite rewards declared techniques, a singleband receiver declaring all seven techniques can outscore a genuinely more resilient system: self-attestation can be gamed by declaration. And apparent fourfold GNSS redundancy reduces, by the definition of a shared common-mode failure domain, to an effective diversity of one. The conclusions hold under a plusor-minus 20 percent perturbation of every driver within the modelled reduction. We argue for reporting per-dimension subscores with provenance and a rank range, not a phantom single number. This is a simulation-derived self-assessment aligned to RPCF v2.0, not a certification. I. INTRODUCTION A recurring gap runs through the PNT-resilience literature. Resilient PNT is a recognised national priority [2], and government, industry, and academic sources converge on the need for a quantified, reproducible, vendor-neutral resilience score checked against a published framework, yet every fielded instrument is self-attestation or pure concept. The DHS Resilient PNT Conformance Framework (RPCF) v2.0 [1] defines maturity Levels 0 to 4 and seven technique categories (Obfuscate, Limit, Verify, Isolate, Diversify, Mitigate, Recover) . The Resist-Detect-Respond-Recover (RDRR) function model [3] frames an assurance case as a documented body of evidence. Yang’s resilient-PNT concept [4] names availability, reliability, continuity, and accuracy criteria. None ships a measurement: a system owner declares conformance, or an assessor scores a questionnaire, and the result is a number nobody can reproduce. This invites a hazard that is rarely examined. To produce a single grade, the multi-dimensional, model-derived reality of a PNT system must be collapsed into one number or one Level. Composite-indicator methodology has long warned that such collapses are sensitive to the (arbitrary) weighting and aggregation choices [8], [9], [10], but the warning has not been quantified for PNT resilience, where the dimensions tradeoff in physically meaningful ways: a

FAQs

Why do existing PNT resilience frameworks struggle to support reproducible scoring?

They primarily rely on self-attestation (checklists, conformance declarations, or questionnaire-style assessment) rather than a measurement engine that produces evidence-backed, reproducible results.

What is the core contribution of the proposed approach?

An open deterministic scoring engine that maps a PNT architecture and its simulated behavior to per-dimension subscores across RPCF categories, RDRR functions, and Yang’s criteria, with each subscore traceable to scenario and oracle and labeled by validation status.

When is a single composite score stable enough for decision-making?

Stability holds in a regime where one design dominates, remaining stable under active denial and near-equal weightings, with winner flips occurring in roughly 1% of draws in the study.

Jul 16, 2026

Upload by Ezra