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Record W4311353305 · doi:10.1049/rsn2.12361

Blind time‐domain motion compensation for synthetic Doppler spectra obtained from an HF‐radar on a floating platform

2022· article· en· W4311353305 on OpenAlexafffund
Sepideh Hashemi, Reza Shahidi, Eric W. Gill

Bibliographic record

VenueIET Radar Sonar & Navigation · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDoppler effectAcousticsRadarAntenna (radio)AutocorrelationPhysicsFrequency domainMotion compensationComputer scienceMathematicsTelecommunicationsAlgorithmComputer visionStatistics

Abstract

fetched live from OpenAlex

Abstract A method for time‐domain motion compensation of high frequency (HF) radar signals for the case of a floating transmitter and fixed receiver is proposed when the motion parameters (including the amplitude and angular frequency of the motion) are not known a priori. In this study, the floating platform is assumed to follow a single‐frequency motion model. Additionally, instead of trying to estimate platform motion parameters from the received motion‐contaminated Doppler spectrum, which is proportional to the observed radar cross‐section of the ocean surface from the floating platform, the motion parameters from the autocorrelation function of the received electric field are estimated, which is related to the received radar cross‐section by application of an inverse temporal Fourier transform. By comparing the locations of the zeros of the autocorrelation function for the fixed antenna case with that for an antenna on a floating platform and finding the zeros associated with the platform motion, the motion parameters are estimated. These parameters are matched with actual motion parameter values, from which the motion‐compensated Doppler spectrum is recovered from the Doppler spectrum of the antenna on a floating platform.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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