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Record W2096688261 · doi:10.1109/systems.2009.4815826

A distributed Kalman filter for actuator fault estimation of deep space formation flying satellites

2009· article· en· W2096688261 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsKalman filterControl theory (sociology)ActuatorDiagonalComputer scienceExtended Kalman filterState-space representationBlock (permutation group theory)State spaceFast Kalman filterInvariant extended Kalman filterRepresentation (politics)AlgorithmMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new distributed Kalman filter scheme is proposed to estimate actuator faults for deep space formation flying satellites. The method can also be applied to large-scale systems such as sensor networks and power systems. For a hierarchical large-scale system, the overlapping block-diagonal state space (OBDSS) representation of the system is transformed into our proposed constrained-state block-diagonal state space (CSBDSS) model. The proposed model becomes purely diagonal which simplifies and allows the distributed implementation of the Kalman filters. The constrained-state condition needs to be satisfied at each Kalman filtering iteration which is shown to be equivalent to solving local constrained optimization cost functions. Simulation results presented confirm the effectiveness of our proposed analytical work.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.610

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 communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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

Quick stats

Citations32
Published2009
Admission routes1
Has abstractyes

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