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Record W2068820155 · doi:10.1049/el.2010.1633

Real-time implementation of mixture particle filter for 3D RISS/GPS integrated navigation solution

2010· article· en· W2068820155 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

VenueElectronics Letters · 2010
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsParticle filterGlobal Positioning SystemWeightingCluster analysisReal-time computingComputer scienceInertial navigation systemFilter (signal processing)Inertial measurement unitTrajectorySimulationArtificial intelligenceMathematicsComputer visionOrientation (vector space)AcousticsTelecommunications

Abstract

fetched live from OpenAlex

An optimised real-time realisation of a mixture particle filter (PF) integrated 3D navigation system for land vehicles based on Global Positioning System (GPS) and Reduced Inertial Sensors System (RISS) is introduced. The PF is a nonlinear filter that can handle errors and uncertainties. Although a mixture PF decreases the number of particles compared to a sampling/importance resampling (SIR) PF, the implementation on embedded systems needs further optimisation to run at higher rates. Introduced is an optimised real-time implementation of a mixture PF on a 600 MHz ARM processor. The optimisation is based on fast median-cut clustering to reduce the complexity of search in the weighting step. The proposed real-time system was tested on a real mobile robot trajectory, showing fast and accurate performance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.455

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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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