Direct One-Bit DOA Estimation Robust in Presence of Unequal Power Signals
Bibliographic record
Abstract
Direction-of-arrival (DOA) estimation is a crucial task in wireless communication and radar systems, with applications spanning beamforming, localization, and target tracking. Conventional methods often require high-resolution quantization, imposing challenges and complexities, particularly in large-scale antenna arrays. One-bit DOA estimation has emerged as a groundbreaking alternative, aiming to achieve accurate results without the need for high-resolution measurements. However, state-of-the-art approaches either require reconstruction of an unquantized covariance matrix or sparse signal recovery, or are based on restrictive assumptions such as the equality of power of signal sources. In this paper, a novel approach for direct one-bit DOA estimation is presented, overcoming the limitations of previous methods by introducing a generalized one-bit covariance matrix and smoothing it. Through analytical and numerical analyses, we reveal the shortcomings of the direct application of the one-bit covariance matrix, particularly in scenarios with unequal signal powers. Comparative simulations demonstrate the superiority of the proposed approach, especially in scenarios with significant signal-to-noise ratio differences and a limited number of snapshots.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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".