A novel interacting multiple model method for nonlinear target tracking
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.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".