Direct path detection using multipath interference cancelation for communication-based positioning system
Bibliographic record
Abstract
Abstract Abstract Recent development in wireless communication-based positioning technology brings significant challenge of detecting the weak signal component arriving from the direct propagation path for time-of-arrival (TOA)-based approach. Due to the common obstruction of the direct propagation path in dense multipath environments, identification of the weak direct path in these environments can be very difficult via the classical correlation-based estimator in the presence of interference from significant later arriving multipath components. A new direct path detection scheme using the multipath interference cancelation is presented in this article. For data communication purpose, an iterative estimator with improved accuracy for joint channel and data estimation is first developed; based on which, the interference of multipath components are reconstructed and subtracted from the original received signal. With the aid of the detected data, an enhanced preamble is formulated. The accuracy of direct path detection is substantially improved by using the correlation between the multipath interference-suppressed signal and the enhanced preamble . Semi-analytical expression of the performance of the iterative estimator is derived. The analysis enables the system to determine an automatic stopping criterion to reduce the computational complexity of the iterative process. The performance of the direct path detection is also analyzed in terms of the signal-to-interference-noise ratio (SINR) and compared with that of the conventional approach. Computer simulation results show the superiority of the proposed direct path detection. The accuracy of the positioning system using the proposed method is also evaluated in dense multipath environments.
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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 | 0.001 |
| 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".