Assessment of a GNSS/INS/Wi-Fi Tight-Integration Method Using Support Vector Machine and Extended Kalman Filter
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Wi-Fi derived positions have been used in the past few years as a complementary source of positioning information for GNSS and Inertial Systems (INS). Ubiquitous positioning that transitions from indoors to outdoors and vice-versa is currently a hot topic of research. In this context, this study aims to analyze the potential of directional antennas sequentially tracking Wi-Fi signals on the 11 channels around the 2.4 GHz frequency in order to serve as an integrated signal for GNSS and INS positioning. Considering, as an example, a single point positioning (SPP) strategy coupled with an INS, the use of directional antennas can be beneficial in order to provide absolute directions of travel by the means of a Support Vector Machine (SVM) lane matching. In order to test the given hypothesis, real-world experiments were performed in areas with and without obstruction in an urban environment. Using a post-processed, smoothed in both forward and backward modes, and finally edited post-processed kinematic (RTK) solution as a reference, the solution integrating SPP GNSS, INS and Wi-Fi was assessed in terms of accuracy. Preliminary results show that such a combination of the directional antennas along with GNSS and INS and their respective SVM and EKF filters, can provide sub-meter accuracy at all times without the need of precise orbits or differential corrections, increasing solution availability, reliability and accuracy on a scalable and cost-effective way.
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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) | 0.001 | 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 it