A novel interacting multiple model method for nonlinear target tracking
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.
Bibliographic record
Abstract
The state estimation of targets is a difficult task, particularly if the target exhibits nonlinear behaviour, which is often the case. Currently, the most popular filters used in target tracking are the Kalman filter (KF) and its various forms, as well as the particle filter (PF). Introduced in 2007, the smooth variable structure filter (SVSF) is a relatively new predictor-corrector method based on sliding mode estimation. In the past, this filter has been used successfully for the state and parameter estimation of mechanical and electrical systems for the purpose of control. This paper introduces a new interacting multiple model (IMM) method that makes use of the SVSF estimation strategy. An air traffic control (ATC) problem is used to compare the common EKF-IMM with the proposed SVSF-IMM in terms of tracking accuracy, robustness, and computational complexity. Furthermore, this paper demonstrates that the SVSF is an effective method for nonlinear target tracking.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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