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Record W2156049697 · doi:10.1109/radar.2009.4976970

Distributed aperture OFDM radar

2009· article· en· W2156049697 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 multiplexingOrthogonalityDiversity schemeComputer scienceElectronic engineeringAntenna diversityFrequency-division multiplexingFast Fourier transformKey (lock)RadarTelecommunicationsAlgorithmEngineeringMathematicsWirelessChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper presents a new method of obtaining frequency diversity using orthogonal frequency division multiplexing (OFDM). Exploiting spatial diversity, the key advantage of a distributed aperture radar, requires orthogonality in, for example, the frequency, time, waveform, dimensions across sensors. This paper focuses on the simplest of these cases; frequency orthogonality. Here we address the key drawback associated with frequency diversity: whereas the use of multiple frequency bands requires additional RF hardware, an OFDM-based system needs only a single oscillator and demodulator while yet maintaining frequency orthogonality. OFDM employs many sub-carriers within a single frequency band instead of occupying different frequency bands. Separation of the signals can be performed oversampling of the incoming signal followed by a Fast Fourier transform (FFT).

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.234

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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