A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
Bibliographic record
Abstract
Multipath fingerprinting is a promising indoor location technique, which contains abundant position features of the received array signals. Effectively representing the position information is one critical issue in fingerprinting localization. Meanwhile, positional accuracy is prone to reduction caused by abnormal measurement readings, which are referred to as outliers, and it has received a little attention in the existing literature. A multipath fingerprinting model for indoor single-site localization is proposed. In this model, the location fingerprint is composed of the spatial-temporal covariance matrix of the multipath signals received by the base station antenna array. The low-dimensional linear subspace of the location fingerprinting is introduced as feature descriptors. Based on the fact that the Grassmann manifold maintains the orthogonality of the linear subspace, the Binet-Cauchy kernel is employed to map the multipath fingerprinting to a higher dimensional reproducing kernel Hilbert space. The Euclidean distance of the nearest point between multipath fingerprinting affine hulls is adopted to represent the similarity of the position. Moreover, an augmented Lagrangian and alternating direction solution is given to remove the influence of outliers. We extensively evaluated the proposed method with the indoor multi-scenario benchmark data set. All the results demonstrate that the location accuracy of the proposed positioning model outperforms the existing method in an indoor environment. As the proportion of outliers increases, the positional accuracy loss of the proposed model is negligible.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".