Implementation and Evaluation of LS-SVM Optimization Methods for Estimating DoAs
Bibliographic record
Abstract
Important technological advancement in designing smart array antennas has been encouraged many researchers to concentrate their work on the two main concepts of the direction of arrival (DoA) and beamforming techniques. The preliminary objective of beamforming techniques includes, electronically, the mainbeam in the direction of interest at a certain time and measuring the output power. In this scenario, the main practical challenge resides in achieving maximum output power in which the direction of steered mainbeam coincides with the direction of arrivals. Since the involved problems in most DoA estimation optimizations consist of a lot of unknown parameters including direction of arrivals, SNRs, signal waveforms and samples of noises in the array output, it may become impossible to build a large enough training dataset for covering the distributions for all the aforementioned test data. An alternative way to overcome this constraint which we aim at stressing in this work involves employing support vector machine algorithms for separating unknown components of the actual input in the higher dimensional feature space. In this work, we have implemented the decision directed acyclic graph (DDAG) and Vapnik-Chervonenkis (VC) methods for the least squares support vector machine (LS-SVM) algorithms for estimating DoAs. We have rigorously verified that DoAs are very much affected the antenna array geometries. In addition, we have investigated the quality of the communication channel by the concept of bit error rate (BER).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".