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Record W2290436574 · doi:10.1049/iet-rsn.2015.0096

Separation and reconstruction of the rigid body and micro‐Doppler signal in ISAR part I – theory

2015· article· en· W2290436574 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 · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsInverse synthetic aperture radarSIGNAL (programming language)Separation (statistics)Doppler effectComputer sciencePhysicsRadarRadar imagingTelecommunications

Abstract

fetched live from OpenAlex

In radar imaging, the micro‐Doppler effect is caused by fast movements of some scattering points on the target. These movements correspond to highly non‐stationary components in the time–frequency domain of the signal. The rigid body can be considered as stationary at one range location during the processing time. This property is used to separate the micro‐Doppler signal from the rigid body using the L‐statistics. Since the rigid body can be considered as a sparse signal, its values can be fully recovered at the positions where the micro‐Doppler and rigid body components overlap. The recovery is based on the compressive sensing theory and methods. After an overview of the methods, a quantitative analysis of the improvements achieved in the time–frequency‐based separation is done. Moreover, a comparison with both the time and the frequency domain analysis is provided. Analysis of small additive noise influence to the reconstruction accuracy is done.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.200

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 head0.012
GPT teacher head0.304
Teacher spread0.293 · 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