Two-Filter Smoothing for Accurate INS/GPS Land-Vehicle Navigation in Urban Centers
Bibliographic record
Abstract
Currently, the concept of multisensor system integration is implemented in land-vehicle navigation (LVN) applications. The most common LVN multisensor configuration incorporates an integrated Inertial Navigation System/Global Positioning System (INS/GPS) system based on the Kalman filter (KF). For LVN, the demand is directed toward low-cost inertial sensors such as microelectromechanical systems (MEMS). Due to the combined problem of frequent GPS signal loss during navigation in urban centers and the rapid time-growing inertial navigation errors when the INS is operated in stand-alone mode, some methodologies should be applied to improve the LVN accuracy in these cases. One of these approaches is to apply smoothing algorithms such as the Rauch-Tung-Striebel smoother (RTSS), which uses only the output of the forward KF. In this paper, the development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced. Two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.001 |
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".