Distributed Time-varying Kalman Filter Design and Estimation over Wireless Sensor Networks Using OWA Sensor Fusion Technique
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Bibliographic record
Abstract
In this paper, a novel estimation procedure is proposed, which consists of designing a distributed class of time-varying Kalman filter based on wireless sensor networks topology along with a new sensor fusion method. The proposed technique is employed to estimate the states and outputs of a linear time-varying system with a high level of accuracy. Both the dynamics of the system and the measurements are assumed to be contaminated by external noises. The notion of Orness and Ordered Weighted Averaging (OWA) operator technique are utilized to fuse the estimation of the sensors. O'Hagan method, along with the gradient descent method, is employed to find the optimal weights. In the introduced approach, OWA weights are learned for each observation such that they efficiently minimize the estimation error for that particular observation. This will result in an outstanding high accurate sensor fusion outcome. In addition, two optimistic and pessimistic exponential OWA operators are used and compared together to achieve a pre-specified level of Orness. The simulation results are shown on a given linear time-varying system to verify the effectiveness of the proposed sensor fusion distributed filtering design method.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it