Comparing an interacting multiple model algorithm and a multiple-process soft switching algorithm: equivalence relationship and tracking performance
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
In this paper, we show that a mathematical relationship exists between the multiple-process soft switching (MPSS) algorithm and an interacting multiple-model (IMM) algorithm. Assuming that each model transition probability is equally likely, the constraints imposed on the mixing and fusion gains in the IMM algorithm are equivalent to those in the MPSS algorithm. Thus, the constraints derived from a probabilistic approach can be reduced to those obtained via a deterministic approach. Unlike the MPSS algorithm with the aid of an additional constraint, the proposed IMM algorithm uses the real-time information of the innovations and their covariances to select filter gains between 0 and 1. The MPSS and the proposed IMM algorithms are applied to tracking a maneuvering target. Simulation results show that the latter has a better position tracking capability than the former when the measurement noise is small or moderate; however, both have similar performance in position tracking when the measurement noise is large. In view of the simulation results, more insights on the performance of MPSS, the proposed IMM and IMM algorithms are provided in the paper.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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