Observability Analysis of Relative Localization Filters Subjected to Platform Velocity Constraints
Bibliographic record
Abstract
This research study performs an observability analysis of the relative localization problem related to multirobotic systems. The study considers different constraints related to the availability of relative position measurements and platform velocity measurements. Constraints related to these measurement sources arise due to several reasons such as, sensing limitations especially in aerial platforms, field of view limitations of sensors, and communication bandwidth limitations that may affect the available measurement rate. Although numerous observability studies are reported for localization of multirobot systems, most of these studies do not investigate the problem under constraints related to platform velocity sensing capabilities, and moreover, these do not investigate the global uniqueness of its results. This paper analyzes observability of the relative localization problem in detail for multiple practical scenarios having limited measurement sources and then extends the study with a global uniqueness analysis of the results. The paper establishes theoretical limitations and design recommendations relevant to relative localization frameworks, which are validated through numerical and experimental evaluations using a multirobot system equipped with relative positioning sensors.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".