Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations
Bibliographic record
Abstract
Abstract Smartphone-based location-based services (LBS) require enhanced horizontal position accuracy with integrity. Due to the mass-market nature and compact design of smartphones, they utilize low-cost antennas and receivers, making them susceptible to multipath effects and other errors, which complicates the differentiation between reliable and unreliable measurements. To address these challenges, this paper explores the application of an adaptive Kalman filter technique to improve smartphone positioning accuracy. Adaptive Kalman filters adjust parameters such as process noise covariance or measurement noise covariance to modify the filter gain. When augmented with outlier detection mechanisms, the filter becomes more robust. This paper introduces a robust adaptive Kalman filter to enhance smartphone position accuracy. Outliers are detected using standardized innovations as a learning statistic, and a t-test is applied to these statistics to identify and mitigate outliers and adapt the measurement noise covariance accordingly. While previous research used empirical values for thresholds to adapt measurement noise covariance matrix, this study derives thresholds from t-tests, contingent on the normal distribution of learning statistics. By eliminating clock reset effects, innovations are transformed from bimodal to a normal distribution. Testing across multiple datasets demonstrates reductions of up to 42% in horizontal positioning root mean square error, with 50th, 68th, and 95th percentile statistics showing improvements of up to 53%, 41%, and 61%, respectively.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".