Interacting Multiple Model Navigation System for Quadrotor Micro Aerial Vehicles Subject to Rotor Drag
Bibliographic record
Abstract
This paper presents the design of an Interacting Multiple Model (IMM) filter for improved navigation performance of Micro Aerial Vehicles (MAVs). The paper considers a navigation system that incorporates rotor drag dynamics and proposes a strategy to overcome the sensitivity of the system to external wind disturbances. Two error state Kalman filters are incorporated in an IMM filtering framework. The first filter has a model that uses conventional Inertial Navigation System (INS) mechanization equations, while the second filter considers a dynamic model with rotor drag forces of the MAV. In order to support the two error state Kalman filters, the generic IMM algorithm [1] is modified for error state implementation, handle dissimilar state definitions, and adaptive switching during operation. Numerical simulations and experimental validation using the EuRoC dataset are conducted to evaluate the performance of the proposed IMM filter design for changing flight conditions and external wind disturbance scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".