Object target tracking using the alpha sliding innovation filter
Bibliographic record
Abstract
The Sliding Innovation Filter (SIF) is an estimation technique developed in 2020 to provide a robust method for estimating a system’s parameters and states in the presence of high modeling uncertainties. This filter ensures that the estimates remain close to the true trajectories. The Alpha-SIF (aSIF), a variant of the SIF introduced in 2022, aims to further smooth the estimates by mitigating the effects of measurement noise. In this work, the aSIF is used to track a ground vehicle navigating within a 2D environment. The angle of maneuver is also estimated using a linearized model that differs from the actual nonlinear model, highlighting the modeling uncertainties. Both measurement and system noise are considered significant, resulting in low signal-to-noise ratios ranging from 15 to 52. The results are compared with the original SIF in terms of Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), and Simulation Time (ST). Findings indicate significant improvements of 12.56% to 49.11% in RMSE and 18.07% in ST, while an 8.07%-13.51% improvement is observed in MAE for the first three states after excluding the error in the initial values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".