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Record W4386803101 · doi:10.23977/acss.2023.070712

User Requirement Analysis of Resilient PNT System

2023· article· en· W4386803101 on OpenAlexvenueno aff
Lijiao Liu, Hong‐Yang Lu, Yanyun Weng, Ya Gao

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceTable (database)Global Positioning SystemComputer securitySystems engineeringTelecommunicationsEngineeringDatabase

Abstract

fetched live from OpenAlex

Firstly, this paper provided a brief introduction to the concepts of Positioning, Navigation and Timing (PNT) and Resilient, and proposed that the fundamental premise for building a resilient PNT system is to identify the diverse needs of users in terms of accuracy, availability, continuity, and other indicators across different typical scenarios. Subsequently, it analyzed user requirements for various scenarios, including aviation routes, maritime navigation, agricultural surveying, train control, vehicle navigation, and emergency response, in different environments such as near-earth, urban, jungle, indoor, and underwater environments. The analysis took into account factors such as the accuracy, availability, continuity, integrity, terminal cost, and form of PNT. Based on this analysis, this paper summarized the user requirements for resilient PNT systems in different scenarios, presenting a comprehensive table of typical user requirements. Furthermore, it suggested that resilient PNT terminals should be cost-effective, compact, low-power, and highly compatible. Finally, the diverse user requirements were summarized and analyzed, providing a research foundation for developing resilient PNT solutions for different typical scenarios, such as near-earth, urban, jungle, indoor, and underwater environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.236
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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