MétaCan
Menu
Back to cohort
Record W1973886409 · doi:10.1109/radar.2012.6212168

Ionospheric clutter model for high frequency surface wave radar

2012· article· en· W1973886409 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClutterRadarRadar horizonComputer scienceRay tracing (physics)Surface waveRemote sensingContinuous-wave radarSkywaveConstant false alarm rateIonosphereGeologyAcousticsRadar imagingTelecommunicationsPhysicsGeophysicsAlgorithmOptics

Abstract

fetched live from OpenAlex

The detection performance of a high frequency surface wave radar (HFSWR) system is primarily limited by clutter, especially ionospheric clutter. Therefore, in order to analyze and/or simulate the capabilities of an HFSWR system a model for the clutter is required. This paper develops and tests a new radio wave propagation theory to model ionospheric clutter. The model is based on path integrals of ray tracing equations which predict the phase power spectrum of clutter due to irregularities in the plasma. This power spectrum is then used to simulate three-dimensional space-time-range radar data cubes. The accuracy of the model is tested by comparing the simulated data to measured data cubes. As an application, the data is then used to evaluate the performance of the newly developed fast fully adaptive processing scheme to mitigate clutter.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.418

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 head0.209
Teacher spread0.187 · 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

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicRadar Systems and Signal ProcessingFrench-language works237,207