Toward Developing an Indoor Localization System for MAVs Using Two or Three RF Range Anchors: An Observability Based Approach
Bibliographic record
Abstract
This study performs a nonlinear observability analysis on radio frequency (RF) range assisted inertial navigation system (INS) for localizing quadrotor micro-aerial vehicles (MAV) in indoor environments. The objective is to use fewer number of RF range nodes as possible to support the efficient scalability of the localization system. The proposed INS formulation incorporates the effect of aerodynamic drag forces, which allows this novel INS to operate without having to use a velocity sensor. The nonlinear observability analysis is carried out for two distinct cases where the range is measured between the MAV and RF anchors placed at known locations. The first case uses three anchors, and for the second case, the analysis is repeated for two range anchors. For each case, different scenarios are considered to identify unobservable conditions of the proposed INS, and the corresponding unobservable modes for those scenarios are determined. These unobservable modes are validated through numerical simulation. The analysis facilitates the range assisted localization of MAVs when there are less than the typical four range configuration and allows planning of the trajectory of the MAV while preserving the observability of the INS. The main contributions of this paper are as follows: 1) nonlinear observability analysis of a range assisted INS for quadrotor MAV with three or two range measurements, 2) theoretical derivation of unobservable trajectories and corresponding unobservable modes, 3) numerical validation of the unobservable modes and experimental validation of the proposed INS performance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".