Performance analysis of fuzzy logic particle filter compared to fuzzy IMM in tracking high-performance targets
Why this work is in the frame
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Bibliographic record
Abstract
A high-performance target may accelerate at non-uniform rates, complete sharp turns within short time periods, thrust, roll, and pitch; which may not follow a linear model. Even though the interacting multiple model (IMM) can be considered as a multimodal approach, it still requires prior knowledge about the target model. To overcome this weakness, a fuzzy logic particle filter (FLPF) is used. It is comprised of single-input single-output; which is presented by fuzzy relational equations. A canonical-rule based form is used to express each of these fuzzy relational equations. The dynamics of the high-performance target are modeled by multiple switching (jump Markov) systems. The target may follow one-out of-seven dynamic behavior model at any time in the observation period under assumption of coordinate turn model. The FLPF has the advantage that it does not require any prior knowledge of statistical models of process as in IMM. Moreover, it does not need any maneuver detector even when tracking a high performance target; which results in less computational complexities. By using an appropriate fuzzy overlap set, only a subset of the total number of models need to be evaluated, and these will be conditioned on acceleration values close to the estimate. This reduces the computational load compared to the fuzzy IMM (FIMM) algorithm. To achieve the whole range of maneuver variables, more models can be added without increasing the computational load as the number of models evaluated is determined only by the overlap. An example is included for visualizing the effectiveness of the proposed algorithm. Simulation results showed that the FLPF has good tracking performance and less computational load compared to the FIMM when applied to systems characterized by large scan periods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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