Characterization of the impact of indoor Doppler errors on Pedestrian Dead Reckoning
Bibliographic record
Abstract
Indoor pedestrian navigation is a very challenging task because self-contained sensors are affected by instrumentation errors corrupting the navigation solution and GNSS signals, which could be used for calibrating the latter, are barely available. Doppler measurements are found to be more accurate than pseudoranges in these harsh indoor environments, especially with narrowband receivers. Therefore the impact of indoor Doppler errors on a Pedestrian Dead Reckoning (PDR) navigation filter is investigated. Doppler errors are simulated using experimental data post-processed with the high sensitivity GSNRx-ss™ software receiver and a derived Doppler error model. Step length and heading errors are simulated for a 500m pedestrian walk. All these controlled errors are introduced in a novel tight PDR/Doppler coupling Extended Kalman filter for assessing the impact of Doppler indoor errors on the navigation solution. It is found that even biased Doppler measurements control the errors' growth in the navigation solution, principally in the attitude angles estimates but also in the norm of the velocity vector. With a 15° error in the walking direction, the horizontal position error equals 10% of the travelled distance for the coupled PDR/Doppler solution and 20% for the MEMS only solution. The analysis highlights also the need for designing new methods to discard outliers in the set of indoor Doppler measurements and benefit from new indoor GNSS observations.
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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".