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Record W2366325673

Performance Analysis of Delay-Doppler Effect in Acquisition of PN Code

2001· article· en· W2366325673 on OpenAlexaff
Atr Key

Bibliographic record

VenueSystems engineering and electronics · 2001
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsPseudorandom noiseDoppler effectFast Fourier transformCode (set theory)Computer scienceEnergy (signal processing)SIGNAL (programming language)Direct-sequence spread spectrumSpread spectrumElectronic engineeringAlgorithmPhysicsMathematicsTelecommunicationsEngineeringCode division multiple accessStatistics
DOInot available

Abstract

fetched live from OpenAlex

In spread-spectrum signal processing,acquisition of pseudonoise(PN)code is commonly implemented by correlation of PN code. In this paper,the effect of time-delay and Doppler frequency in acquisition of PN code is analyzed, using maximal-length shift register sequence as research object. It is shown that delay-Doppler cross effect is neglectable in most cases. Furthermore, an acquisition method of PN code using FFT to accumulate energy is presented. The method can compensate Doppler frequency,and two-dimensional joint estimate of delay-Doppler can be obtained.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.185
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2001
Admission routes1
Has abstractyes

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