An efficient INS/GPS impulse response model for bridging GPS outages
Bibliographic record
Abstract
The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) architectures can be achieved through the use of many time-domain filters such as, extended Kalman, unscented Kalman, divided difference, and particle filters. The main objective of these filters is to achieve precise fusion of the data from GPS and INS to provide INS-only navigation solution during GPS outages. The prediction mode performance of all state-of-the-art time-domain filters is poor with significant drift in the INS-only solution. In this paper, a new frequency-domain dynamic response method is proposed to model the INS/GPS system. The input to this dynamic system is the INS-only solution and the output is the INS/GPS integration solution that help derive the transfer function. The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system. It is shown that the long-term motion dynamics are recovered by 72%, 42%, 75%, and 40% for north velocities, east velocities, north positions, and east positions respectively when compared with INS-only solution (prediction mode of the INS/GPS filter).
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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 | 0.000 |
| 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 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".