Direction-of-arrival estimation of an amplitude-distorted wavefront
Bibliographic record
Abstract
In a number of array signal processing applications, such as underwater source localization, the propagation medium is not homogeneous, which causes a distortion of the wavefront received by the array. There has been some interest in the direction-of-arrival (DOA) estimation of such distorted wavefronts. Most works on this problem considered the so-called multiplicative noise scenario based on the rather strong assumption that the distortion is random and can be parameterized by a small number of parameters. To gain robustness against mismodeling, we assume a scenario in which the wavefront amplitude is distorted in a completely arbitrary way. Our main contribution consists of showing how to eliminate all nuisance (distortion) parameters from the likelihood function corresponding to such a scenario and obtain a robust maximum likelihood DOA estimate by means of a simple one-dimensional (1-D) search. Despite its simplicity, it is shown that the estimator has a performance close to the Cramer-Rao Bound (CRB), for which we derive a closed-form expression. Moreover, its accuracy is comparable with that of estimators that require knowledge of the form of amplitude distortions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".