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Record W2979768185 · doi:10.1049/joe.2019.0751

Method to suppress narrowband interference for OFDM radar

2019· article· en· W2979768185 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

VenueThe Journal of Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsOrthogonal frequency-division multiplexingComputer scienceRadarElectronic engineeringInterference (communication)NarrowbandTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off‐the‐shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency‐modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi‐definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft‐thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method.

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)0.001
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: none
Teacher disagreement score0.756
Threshold uncertainty score0.317

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 studies0.0000.000
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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