TOA Estimation Enhancement based on Blind Calibration of Synthetic Arrays
Bibliographic record
Abstract
The accuracy of time of arrival (TOA) measurements is significantly compromised due to the multipath components which coexist with the desired line of sight (LOS) components in the received signal. Effective mitigation of multipath is offered by using an antenna array with beamforming capabilities. However, arrays are physically large and unwieldy from a signal processing perspective and hence not suitable for a small handheld device. In this paper, a receiver consisting of a single antenna that is moved arbitrarily in space forming a synthetic antenna is considered. Blind calibration of the resultant spatial array is achieved through the use of fourth order cummulants. As demonstrated in this paper, a synthetic array, based on this blind calibration method can practically enhance the LOS signal by suppressing multipath through spatial filtering and thereby improve the TOA performance. Monte Carlo simulations are performed to demonstrate the performance of the proposed method.
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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 | 0.000 |
| 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 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".