An enhanced, Robust, adaptive Kalman filter for continuous urban navigation with low-cost sensors
Bibliographic record
Abstract
Abstract Low-cost sensor navigation has been on the rise in the past decade with the onset of many modern applications that demand decimetre-level accuracy using mass market sensors. The key advantage of Precise Pointing Positioning (PPP) technique over Real-Time Kinematic (RTK) is the non-requirement of local infrastructure and still being able to attain decimetre to sub-metre level accuracy while using mass market low-cost sensors. Achieving dm to submetre-level accuracy is a challenge in urban environments. Therefore, adaptive filtering needs to be implemented along with low-cost sensors motion based constraining and atmosphere constraints. The traditional robust adaptive Kalman filter (RAKF) uses empirical limits that are derived by analyzing the GNSS receiver data beforehand to determine when the adaptive factor needs to be applied. In this research, a novel technique is proposed to determine the adaptive factor computation based on the detection of increase in the number of satellite signals after a partial outage, independent of using the traditional empirical values. The proposed method provides 38-55% better accuracy than the traditional RAKF and proves to be a significant improvement for the next generation applications, such as low-autonomous, virtual reality and others.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".