Enhancing LOS/NLOS Classification in UWB with Robust Feature Engineering
Bibliographic record
Abstract
This paper presents a robust feature engineering approach to improve the classification of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) signals in Ultra-Wideband (UWB) systems, which are widely used for accurate indoor localization. UWB technology, known for its high bandwidth and precision, relies heavily on distinguishing LOS from NLOS signals to maintain accurate positioning. Traditional classification methods often fall short in handling the complexities of NLOS environments due to limited feature extraction and minimal data preprocessing, leading to reduced model generalizability. To address these challenges, we conducted an extensive exploratory data analysis (EDA) to identify significant CIR (Channel Impulse Response) and non-CIR features for LOS/NLOS classification, followed by outlier detection using Mahalanobis distance and dimensionality reduction through Principal Component Analysis (PCA). Three datasets, i.e., raw data, standard-scaled selected features, and PCA-transformed features, were evaluated using common machine learning classifiers. Experimental results show that our feature engineering approach significantly enhances model performance, with the PCA-transformed dataset achieving the highest accuracy, precision, recall, and F1-score across all classifiers, demonstrating the substantial impact of engineered features on UWB signal prior to LOS/NLOS classification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".