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Record W2575506202

Analysis of range walk in distributed MIMO radar

2016· article· en· W2575506202 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

VenueEuropean Radar Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsMIMORange (aeronautics)Bistatic radarTransmitterRadarComputer scienceLow probability of intercept radarPhysicsAlgorithmAcousticsElectronic engineeringRadar imagingTelecommunicationsEngineeringAerospace engineeringChannel (broadcasting)
DOInot available

Abstract

fetched live from OpenAlex

The detection of high velocity targets with a Distributed MIMO radar is considered. An expression for probability of target detection is derived while accounting for range walk degradation due to target motion. This derivation enables the explicit calculation of the extent of range walk for each transmitter-receiver pair. The dependence of decreasing probability of detection on decreasing bistatic angle, decreasing radial angle, or increasing target velocity magnitude is quantified. Examples are presented to demonstrate the variation in detection performance due to range walk for different transmit-receive paths, for a single target with constant velocity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.207
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