Computationally Efficient DOA and Polarization Estimation of Coherent Sources with Linear Electromagnetic Vector-Sensor Array
Bibliographic record
Abstract
This paper studies the problem of direction finding and polarization estimation of coherent sources using a uniform linear electromagnetic vector-sensor (EmVS) array. A novel preprocessing algorithm based on EmVS subarray averaging (EVSA) is firstly proposed to decorrelate sources' coherency. Then, the proposed EVSA algorithm is combined with the propagator method (PM) to estimate the EmVS steering vector, and thus estimate the direction-of-arrival (DOA) and the polarization parameters by a vector cross-product operation. Compared with the existing estimate methods, the proposed EVSA-PM enables decorrelation of more coherent signals, joint estimation of the DOA and polarization of coherent sources with a lower computational complexity, and requires no limitation of the intervector sensor spacing within a half-wavelength to guarantee unique and unambiguous angle estimates. Also, the EVSA-PM can estimate these parameters by parameter-space searching techniques. Monte-Carlo simulations are presented to verify the efficacy of the proposed algorithm.
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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 | 0.001 |
| 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".