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Record W4291916233 · doi:10.3390/rs14163915

The Dynamic Sea Clutter Simulation of Shore-Based Radar Based on Stokes Waves

2022· article· en· W4291916233 on OpenAlexfundno aff
Feng Luo, Yao Feng, Guisheng Liao, Linrang Zhang

Bibliographic record

VenueRemote Sensing · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsnot available
FundersMcMaster University
KeywordsClutterRadarGeologyAcousticsWind waveComputer scienceRadar horizonRemote sensingRadar imagingPhysicsContinuous-wave radarTelecommunications

Abstract

fetched live from OpenAlex

The sea clutter model based on the physical sea surface can simulate radar echo at different times and positions and is more suitable for describing dynamic sea clutter than the traditional models based on statistical significance. However, when applying the physical surface model to shore-based radar, the effects of wave nonlinearity, breaking wave, shadow, and radar footprint size must be considered. In this paper, a dynamic sea clutter simulation scheme based on a nonlinear wave is proposed that uses random Stokes waves instead of linear superposition waves to simulate the nonlinear dynamic sea surface and then calculates echo in the form of scattering cells. In this process, the relationship between wind speed and the nonlinear factor of the Stokes wave is derived, a simple model of shadow modulation is provided, and a method for appending the sea clutter spikes formed by breaking waves is developed. The experimental results show that the simulated sea clutter and the real measured clutter have good consistency in intensity, amplitude statistical distribution, Doppler spectrum, and spatiotemporal correlation. The proposed scheme is suitable for the sea clutter simulation of shore-based radar and can also adjust the relevant parameters to extend to other types of sea clutter simulation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.614

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)0.0000.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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