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Record W1505394098 · doi:10.1049/iet-rsn.2010.0249

Doppler measurement accuracy in standard and high-sensitivity global navigation satellite system receivers

2011· article· en· W1505394098 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Radar Sonar & Navigation · 2011
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDoppler effectComputer scienceSensitivity (control systems)Global Positioning SystemSatelliteBlock (permutation group theory)Variance (accounting)Doppler frequencyNoise (video)Electronic engineeringRemote sensingTelecommunicationsMathematicsEngineeringPhysicsGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

Doppler frequency estimates generated by a global navigation satellite system receiver are essential for the evaluation of the user velocity. In this study, a theoretical framework allowing the evaluation of Doppler measurement accuracy is introduced. The variance of Doppler estimates is related to the carrier-to-noise density power ratio (C/N0) and the type of processing adopted by the receiver. Both standard sequential and high-sensitivity receivers adopting block processing techniques are considered. A general formula quantifying the Doppler variance is derived and applied to these two receiver architectures. Moreover, the concept of Doppler bandwidth is introduced for quantifying the amount of input noise transferred to the final Doppler estimates. The generality of the theory is validated using live GPS data and a good agreement between theoretical and empirical results is found.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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