High sensitivity multi‐channel digital receiver for wideband very weak signal direction‐finding classified by machine learning
Bibliographic record
Abstract
Abstract For weak signal detection with direction‐finding (DF), this article presents a new receiver design approach that combines our accumulatively increasing receiver sensitivity (AIRS) signal detection algorithm with the compressive‐sensing (CS)‐based DF‐array/algorithm. The former uses the concept of timeslot (TS)‐based signal threshold detection, whereas the latter employs a frequency‐independent array with randomly located elements, whose bandwidth (BW) largely determines the DF‐array BW. To estimate the direction of a signal, the AIRS algorithm generates the array steering vectors in each TS when the amplitude of any frequency bins exceeds the predetermined threshold of the TS. The aim of this paper is to demonstrate the ability of the new receiver to detect low probability of intercept radar signals with high DF accuracy, fine frequency resolution, and good time‐of‐arrival measurement resolution. To discriminate accurate emitter directions from many false estimations created by the DF‐array in very low signal‐to‐noise ratio environments, K‐means clustering was also applied. In a scenario, the frequency modulated signals from several 165‐mW X‐band radars were in the field of view of a 6‐element DF‐array. Simulation results show that the receiver can accurately estimate all the emitters' directions with root mean squared error of less than 1°, when the separation between the DF‐array and radars is about 100 km.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".