Strengthening Lattice Kalman Filters: Introducing Strong Tracking Lattice Filtering for Enhanced Robustness
Bibliographic record
Abstract
This work develops a novel formulation of the lattice Kalman filter (LKF) for enhanced robustness. This novel approach initially integrates the concept of sliding innovation to refine the measurement update phase of the LKF, ensuring that the filter’s innovation is constrained within predetermined bounds; the resultant robust filter is designated as the Bounded Innovation Lattice Kalman Filter (BILF). To enhance its numerical stability and adaptive response to rapid changes in the process model or observational data, a Jacobian-free formulation of BILF with a time-varying bounded layer is first developed and then augmented with the adaptive fading factor strategy, leading to the establishment of a robust estimation method, termed as Strong Tracking LKF (ST-LKF). The developed estimation algorithm, in comparison with several renowned filters, is applied to the real-time estimation of states and output power of a single-machine infinite bus (SMIB) system under significantly noisy conditions. The effectiveness of ST-LKF is rigorously tested against a spectrum of operational conditions, including time-variant step and/or ramp inputs, measurement outliers, and short circuits, encompassing both stable and unstable states. Simulation results validate that the proposed filtering strategy excels in terms of accuracy and robustness when faced with model uncertainties and extreme noise levels, consistently maintaining its performance in estimating states across different designed scenarios.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".