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Record W2108740991 · doi:10.1109/icassp.2008.4518175

Constrained linear least squares approach for TDOA localization: A global optimum solution

2008· article· en· W2108740991 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

VenueProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing · 2008
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMultilaterationMathematical optimizationLeast-squares function approximationCommon emitterComputer scienceTotal least squaresLinear least squaresNoise (video)AlgorithmNon-linear least squaresGeneralized least squaresQuadratic growthMathematicsEstimation theoryStatisticsEngineeringElectronic engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we formulate the time-difference-of-arrival (TDOA) emitter localization problem as a quadratically constrained linear least squares problem. We show that the constrained least squares problem has a unique global minimum and develop a computationally efficient algorithm for finding the emitter location estimates that corresponds to the global minimum. The approach is robust and more resilient to moderate and large sensor measurement noise than other existing TDOA location techniques. Computer simulations are used to demonstrate the effectiveness and performance of the proposed algorithm.

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 score0.951
Threshold uncertainty score0.746

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 head0.040
GPT teacher head0.263
Teacher spread0.224 · 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