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Record W2790307162 · doi:10.1109/taes.2018.2805228

Robust Clutter Rejection in Passive Radar via Generalized Subband Cancellation

2018· article· en· W2790307162 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

VenueIEEE Transactions on Aerospace and Electronic Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsClutterComputer scienceRadarOffset (computer science)Constant false alarm rateSubspace topologyPassive radarAlgorithmCarrier frequency offsetFrequency offsetElectronic engineeringContinuous-wave radarEngineeringArtificial intelligenceRadar imagingTelecommunicationsOrthogonal frequency-division multiplexing

Abstract

fetched live from OpenAlex

Passive radar is known to suffer from high clutter. In this paper, a generalized subband cancellation (GSC) algorithm is proposed to achieve robust clutter rejection. Four major factors are taken into account, namely carrier frequency offset, sampling frequency offset, fractional delay, and hardware frequency response. The GSC algorithm utilizes subband signal processing and tailored clutter subspace construction to solve the problems caused by these factors. Quantitative evaluations demonstrate that the GSC algorithm approaches the optimal clutter cancellation performance. Its effectiveness is also validated using field experimental data.

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 score0.576
Threshold uncertainty score0.788

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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