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Record W3120403041 · doi:10.2514/6.2021-1398

A Geometric Model for Estimating Time Difference of Arrival (TDOA) Performance

2021· article· en· W3120403041 on OpenAlex
Thomas Frey, S. J. Ford

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

VenueAIAA Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMultilaterationCramér–Rao boundHyperbolaComputer scienceIntersection (aeronautics)FDOARangingDwell timeTime of arrivalCommon emitterDilation (metric space)AlgorithmStatisticsMathematicsEstimation theoryGeometryElectronic engineeringTelecommunicationsEngineeringWireless

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-1398.vid Time difference of arrival (TDOA) is a multi-ship passive ranging technique that provides a very accurate location estimate without the need for a directional antenna or long dwell durations. TDOA is more robust in the type of emitters that can be located and does not require cooperation from the emitter itself. Existing methods for estimating the TDOA accuracy are based on the Cramer Rao Lower Bound (CRLB); however, these estimates are often optimistic, particularly when the measurement errors are biased. A geometric model is presented for estimating TDOA accuracy based on the intersection of the polar angles of the hyperbolas that yields a more accurate estimate of location error in the presence of primarily biased measurements. This model captures the dilation of accuracy with geometry and is convenient for effects-based simulation or performance analysis.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.624

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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