GPS-denied Navigation: Attitude, Position, Linear Velocity, and Gravity\n Estimation with Nonlinear Stochastic Observer
Bibliographic record
Abstract
Successful navigation of a rigid-body traveling with six degrees of freedom\n(6 DoF) requires accurate estimation of attitude , position, and linear\nvelocity. The true navigation dynamics are highly nonlinear and are modeled on\nthe matrix Lie group of SE2(3). This paper presents novel geometric nonlinear\ncontinuous stochastic navigation observers on SE2(3) capturing the true\nnonlinearity of the problem. The proposed observers combines IMU and landmark\nmeasurements. It efficiently handles the IMU measurement noise. The proposed\nobservers are guaranteed to be almost semi-globally uniformly ultimately\nbounded in the mean square. Quaternion representation is provided. A real-world\nquadrotor measurement dataset is used to validate the effectiveness of the\nproposed observers in its discrete form. Keywords: Inertial navigation,\nstochastic system, Brownian motion process, stochastic filter algorithm,\nstochastic differential equation, Lie group, SE(3), SO(3), pose estimator,\nposition, attitude, feature measurement, inertial measurement unit, IMU.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".