Comparison of forward Vs. feedback Kalman filter for aided inertial navigation system
Why this work is in the frame
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Bibliographic record
Abstract
An autonomous underwater vehicle (AUV) can employ the inertial navigation system (INS) to determine its position. The measurement from the INS, which utilizes the accelerometers and gyroscopes, deteriorates with time due to the accumulation of errors in the sensors. To reduce the error growth in the INS, an external aiding source such as the DGPS or the differential GLONASS can be utilized. The loosely coupled system approach is usually implemented in integrating the information from the aiding source and the INS. The two main architectures in the loosely coupled systems are the forward Kalman filter (FKF) and the direct feedback Kalman (DKF) filters. In this paper we explore the possibility of using these techniques for an aided INS in the AUV application. The advantages and the disadvantages of using the FKF and the DKF are also discussed.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it