Time delay estimation for UWB non coherent receiver in indoor environment, from theory to practice
Bibliographic record
Abstract
Extracting parameter estimates from noisy observations of an underlying signal is a common problem in many fields. Time delay estimation (TDE) is essential for many areas, such as localization, array processing, and radar. The performance of any estimator is often evaluated via the mean square error (MSE) that can then be compared to analytical MSE lower bounds. In this paper, we first analyze a maximum likelihood (ML) estimator based on the knowledge of noisy second order statistics of the channel. We investigate lower bounds for the time delay estimation error for ultra-wideband ranging systems operating in realistic multipath environments. Based on the Cramer-Rao lower bound (CRLB), we derive analytically a lower bound of the time delay estimation calculated using the Karhunen–Loève decomposition of the estimated channel autocorrelation matrix. Also, we investigate the practical implementation (based on energy detection) of the time delay estimator. In the second part of the paper, we have analyzed the time delay estimation performances with the energy maximization receiver. Simulations are evaluated using a simulated UWB underground mine channel. This can be considered as the first step for a global positioning system for use mining industry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".