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Record W2171364296 · doi:10.1109/radar.2008.4721071

Orthogonal frequency division multiplexing in distributed radar apertures

2008· article· en· W2171364296 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
KeywordsOrthogonal frequency-division multiplexingDiversity schemeComputer scienceFast Fourier transformElectronic engineeringRadarTransmission (telecommunications)Frequency-division multiplexingDemodulationAntenna diversityWirelessTelecommunicationsAlgorithmEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

In previous work, frequency diversity has been shown to be an effective means of exploiting distributed radar apertures. Frequency diversity allows for each transmission to be isolated and processed independently of other frequencies. This paper presents an efficient implementation of a frequency diverse distributed radars, based on orthogonal frequency division multiplexing (OFDM), a concept borrowed from wireless communications. OFDM allows for frequency diversity to be implemented using only a single modulator/demodulator with the transformation, using a fast Fourier transform (FFT) to move between time and frequency domains as required. The resulting system is significantly simpler, but, as we will show, OFDM places a stringent constraint on the allowed sampling rate.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.415

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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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