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Record W2371426722

A Method of Radar Realizing CFAR Detection Based on DSP Chip

2005· article· en· W2371426722 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

VenueModern Radar · 2005
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsClutterConstant false alarm rateRadarDigital signal processingRadar signal processingComputer scienceContinuous-wave radarSignal processingElectronic engineeringArtificial intelligenceRadar imagingEngineeringTelecommunicationsComputer hardware
DOInot available

Abstract

fetched live from OpenAlex

CFAR detection of radar targets in heavy clutter background is an important part in radar signal processing. With appearance of LSI and high capability DSP, DSP technology is used in radar signal processing widely. How to realize signal processing effectively basing on high capability DSP is an important problem that radar engineers must consider. In this paper, the characteristics of ADSP21160 are introduced first, then the principle of radar CFAR detection in Rayleigh distributed clutter is discussed, and the way of realizing radar CFAR detection in clutter background based on ADSP21160 is proposed, and the results of simulation are provided at last.

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: Methods · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.721

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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