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Record W2992923608

Estimating Torpedo range using multi-path signals and fast orthogonal search techniques

2007· article· en· W2992923608 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsTorpedoRange (aeronautics)Path (computing)Reflection (computer programming)MATLABAlgorithmAcousticsSonarEngineeringComputer sciencePhysicsAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces a torpedo range estimation algorithm, which is primarily developed in MATLAB. The Torpedo Detection Algorithm (TDA) employs the fast orthogonal search (FOS) algorithm for high-resolution spectral analysis to detect the closely spaced direct-path and surface-reflection signals. When a direct-path and surface-reflection are found, an automatic alert of a possible torpedo detection is initiated. In simulation, the existence of a torpedo and its range are found as it travels from 5000 to 750 metres from the receiver. Simple trigonometric expressions are used to estimate the torpedo's range given the two frequencies estimated by FOS and a priori information about the torpedo speed and depth. The predicted and actual ranges for a simulation in which a torpedo approaches from 5000 to 750 metres is shown.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.975

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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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