A Two-Stage Approach to Estimate the Angles of Arrival and the Angular Spreads of Locally Scattered Sources
Bibliographic record
Abstract
We propose a new two-stage approach to estimate the nominal angles of arrival (AoAs) and the angular spreads (ASs) of multiple locally scattered sources using a uniform linear array (ULA) of sensors. In contrast to earlier works, we consider both long- and short-term channel variations, typically encountered in wireless links. In the first stage, we exploit sources independence to blindly estimate the channel over several data blocks regularly spaced by intervals larger than the coherence time but each, short enough in length, to make time variations negligible within the block duration. We, thereby, decouple the multisource channel parameters estimation problem in hand into parallel and independent single-source channel parameters estimation subproblems. In the second stage, for each spatially scattered source, we process the corresponding sequence of quasi-independent channel realization estimates as a new single-scattered-source observation over which we apply Taylor series expansions to transform the estimation of the nominal AoA and the AS of the corresponding scattered source into a simple localization of two closely spaced, equi-powered, and uncorrelated rays (i.e., point sources). To localize both rays, we propose new accurate and computationally simple closed-form expressions for the mean value of the spatial harmonics and their separation by means of covariance fitting. An asymptotic performance analysis is also provided to prove the efficiency of the proposed estimators. Then, the AS and the nominal AoA of every source are directly deduced. The whole proposed framework takes advantage of the capabilities of the preprocessing channel identification step (to reduce the noise effect and decouple the estimation of the channel parameters of every source from the others) and the new simple and accurate closed-form estimators to accurately retrieve the channel parameters even in the most adverse conditions, mainly low signal-to-noise ratio (SNR), few sensors, no prior knowledge of the angular distribution, and closely spaced sources, as supported by simulations.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".