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Record W2014951544 · doi:10.1049/ip-rsn:20041236

Denial of bistatic hosting by spatial–temporal waveform design

2005· article· en· W2014951544 on OpenAlexaff
Hugh Griffiths, Michael C. Wicks, D. Weiner, Raviraj Adve, Paul Antonik, I. Fotinopoulos

Bibliographic record

VenueIEE Proceedings - Radar Sonar and Navigation · 2005
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBistatic radarComputer scienceContinuous-wave radarWaveformRadarPassive radarRadar engineering detailsRadar imagingRemote sensingAcousticsTelecommunicationsElectronic engineeringGeologyPhysicsEngineering

Abstract

fetched live from OpenAlex

A set of theoretical techniques to prevent a radar from being used by a bistatic radar receiver as a non-cooperative illuminator are analysed. This works by radiating in addition to the radar signal waveform, a ‘masking signal’ waveform which is orthogonal to the radar signal waveform, both in the coding domain and the spatial domain. A number of different coding schemes are analysed. Two spatial coding methods are presented and analysed: the first uses a pair of interferometer elements at the extremities of the radar antenna array; the second uses a Butler matrix to generate a set of orthogonal beams. System-level calculations are presented to show the level of masking of the radar signal received by a bistatic radar receiver, and the suppression of the masking signal in the host radar echo. Some ideas for further work are presented.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2005
Admission routes1
Has abstractyes

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