Three-Dimensional Source Localization Based on 1-D AOA Measurements: Low-Complexity and Effective Estimator
Bibliographic record
Abstract
Three-dimensional (3-D) source localization based on 1-D angles of arrival (AOA) measurements from multiple linear arrays have attracted much attention due to the low cost of linear arrays and the easy access to 1-D AOAs. However, most existing methods are restricted to specific array deployments, and the convex optimization methods applicable to the linear array with arbitrary deployments have drawbacks such as high computational complexity and nonportability to small industrial equipment. In this article, we propose two 1-D AOA-based source localization methods. The first method uses the iterative reweighted least square (IRLS) to obtain a coarse solution and a deviation refinement (DR) procedure to refine the estimation. Each step in IRLS-DR has a closed-form solution; thus, it is computationally efficient. Moreover, it applies to arbitrary interarray deployments, and theoretical analysis shows that its performance can approach the Cramér–Rao lower bound (CRLB) at small noise levels. The second method addresses the maximum likelihood estimation (MLE). A modified Levenberg–Marquardt (MLM) method is proposed, which focuses on designing a new selection strategy of the regularization parameter to realize a high convergence probability and localization accuracy. Experiments based on computer simulations and SWellEx96-S59 ocean acoustic measurements are carried out to verify the performance and advantages of the proposed methods.
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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".