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Record W2111228703

An algorithm for multitarget tracking with multiple asynchronous bearings-only sensors

2009· article· en· W2111228703 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

VenueAdelaide Research & Scholarship (AR&S) (University of Adelaide) · 2009
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsAUG Signals (Canada)
Fundersnot available
KeywordsTracking (education)Computer scienceAsynchronous communicationComputer visionAlgorithmPosition (finance)Artificial intelligenceCartesian coordinate systemTrack (disk drive)Mathematics
DOInot available

Abstract

fetched live from OpenAlex

An algorithm is developed for tracking multiple targets using distributed bearings-only sensors. It is assumed that the sensors report the measurements asynchronously and the processing is done centrally. The proposed algorithm first forms bearings-only (mono) tracks for each sensor and then combines them to form Cartesian position (stereo) tracks. The stereo tracks are initialized using a multidimensional assignment technique. Once the stereo tracks are initialized the mono tracks contributed to the stereo tracks are deleted and the stereo tracks are updated directly using the measurements from the sensors. As shown later in this paper the proposed algorithm is computationally simple and can provide better tracking performance compared to an existing algorithm. Simulations carried out to track multiple targets confirm the effectiveness 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.005
Open science0.0030.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.307
Teacher spread0.262 · 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