Use of XM <sup>TM</sup> radio satellite signal as a source of opportunity for passive coherent location
Bibliographic record
Abstract
This article investigates the use of an XMTM signal as a source of opportunity for passive coherent location. An analysis of the echo-to-direct signal ratio and the echo-to-noise ratio emphasises two major target detection problems: the masking effect of the direct signal and the low power of the echo from the reflected signal. First, a subspace-based method is proposed to suppress the direct signal by the projection of the received signal in subspaces orthogonal to direct signal. Then, to overcome the problem of low power of the echo, a directive gain technique is also proposed: an overlapped array is used to provide a directive gain while maintaining a sufficient resolution for using the subspaces method. The number of subarrays employed is optimised for maximum gain while avoiding the occurrence of nuls at the output of a filter matched to the direct path signal, for specific numbers of subarrays. The impact of the subtraction of the noise covariance matrix of noise is also studied. The integrated detection system is then tested through simulations to verify its effectiveness for the detection of moving targets.
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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 | 0.000 | 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 | 0.000 |
| 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".