Distributed Widely Linear Multiple-Model Adaptive Estimation
Bibliographic record
Abstract
The paper considers the problem of estimating the state of a complex-valued stochastic hybrid system observed distributively using an agent/sensor network (AN/SN) with complex-valued (possibly noncircular) observations. In several distributed estimation problems, a suitable model to describe the underlying system is unknown a priori, i.e., distributed state estimation with structural uncertainty. Motivated by application of widely linear processing techniques in such problems, the paper proposes a class of distributed multiple-model adaptive estimation algorithms, referred to as the CD/MMAE. By incorporating the particular structure of the complex-valued observations on the second moment, first we develop two hierarchical CD/MMAE implementations and then use them as the building blocks and develop a diffusion-based hybrid estimator for decentralized estimation without incorporation of a fusion centre. The paper derives a new form of the adaptation law and a new form of information fusion, which takes advantage of the full second-order statistical properties of the underlying observations. Convergence properties of the proposed diffusion-based CD/MMAE are then investigated. We show that the adaptive weight of all local nodes converges to the true mode with probability one. Simulation results indicate that the proposed hybrid estimators provide improved performance and convergence properties over their traditional counterparts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".