Wind-field mapping using a coupled state and wind-velocity estimator for fixed-wing unmanned aerial vehicles
Bibliographic record
Abstract
For fixed-wing unmanned aerial vehicles (UAVs), knowledge of both the instantaneous wind velocity as well as the overall wind field are of interest.Knowledge of the instantaneous wind velocity can be used in the control and guidance strategies for fixed-wing UAVs to reject wind disturbances.In addition, the instantaneous wind-velocity estimates can be used to generate a mapping of a spatially-varying wind field.Once the overall wind field has been estimated, it can then be used to plan energy-efficient paths and trajectories to help achieve longer distance and more efficient flight.This thesis first explores the dynamics, guidance, and control of fixed-wing UAVs, and then investigates coupled state and wind-velocity estimation using standard sensors onboard a fixed-wing UAV.An SO(3)based attitude controller is used in conjunction with a nonlinear guidance law to control the UAV in three-dimensional space.For state estimation, the invariant extended Kalman filter (IEKF) framework, a recently introduced method for nonlinear state estimation on matrix Lie groups, is applied to the problem of estimating both the UAV states and wind velocity simultaneously.Both left-and right-invariant versions of the IEKF (denoted the LIEKF and RIEKF respectively) are derived for the problem of coupled state and wind-velocity estimation.The LIEKF and RIEKF are compared to a traditional multiplicative EKF under a variety of scenarios.Finally, estimates of the instantaneous wind velocity are used in a mapping framework using Gaussian process (GP) regression, to generate a map of a spatially-varying wind field.The effects of sensor noise as well as turbulence intensities on the mapping solution are investigated. Chapter 4 -Solving the tightly coupled state and wind velocity estimation problem using both a left-and right-invariant framework.The SO(3)-based attitude controller for fixed-wing UAVs, as well as a similar version of the left-invariant extended Kalman filter for coupled state and wind estimation, was also presented in [1].All text, plots, figures and results in this thesis are produced by Mitchell Cohen.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".