Fuzzy logic based particle filter for tracking a maneuverable target
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 propose a new fuzzy logic-based particle filter (FLPF) algorithm for tracking a maneuvering target. The nonlinear system which is comprised of two-input and single-output are presented by fuzzy relational equations. Each of the fuzzy relational equations is expressed in a canonical-rule based form. The dynamics of the maneuvering target are modeled by multiple switching (jump Markov) systems. We assume that the target follows one-out-of-three dynamic behavior model at any time in the observation period: constant velocity (CV) motion model, clockwise coordinated turn (CCT) model, and anticlockwise coordinated turn (ACT) model. The time-varying deviation between actual and predicted positions is inferred by a two-input single-output fuzzy relation. An example is included for visualizing the effectiveness of the proposed algorithm. For comparison purposes, we simulated a conventional sequential importance sampling (SIS) algorithm. Simulation results showed that the FLPF has better tracking performance compared to the SIS.
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 | 0.000 | 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 | 0.000 | 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