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Record W2579032995 · doi:10.1109/iccais.2016.7822453

A real time on orbit calibration for redundant gyros of launch vehicle upper stage

2016· article· en· W2579032995 on OpenAlexfundno aff
Tong Zhang, Fen Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsnot available
FundersCanadian Space AgencyNational Natural Science Foundation of China
KeywordsCalibrationInertial navigation systemSpacecraftOrbit (dynamics)Control theory (sociology)Kalman filterAccelerometerAttitude and heading reference systemSatelliteInertial measurement unitRocket (weapon)Compensation (psychology)Computer scienceInertial frame of referenceExtended Kalman filterAttitude controlSpace vehicleAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

With regards to the fourth stage rocket, upper stage flight time can be up to 48 hours. Volatile space environment leads parameters calibrated in ground do not meet the requirements of the spacecraft precise orbit. In this paper, a real-time on-orbit calibration method of redundant laser gyro inertial measurement unit for space transfer vehicle is proposed. We make use of the high precise attitude information from star sensor to design a calibration model with ten inertial instruments (five gyros and five accelerometers), which includes gyros drift, and scale factor error. The redundant gyros of strapdown inertial measurement unit parameters are estimated through Kalman filter and Sage-Husa adaptive filter. For this, we conduct numerical simulation. That the result of the parameters calibration precision is within 5% demonstrates the validity of this method on satisfying the navigation system's reliability and accuracy with the estimation and compensation for gyros error. The on-orbit calibration method provides a theoretical reference for the higher precise attitude determination of space transfer vehicle.

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.

How this classification was reachedexpand

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 score0.052
Threshold uncertainty score0.271

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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