Enhanced Differential Detection Scheme for Weak GPS Signal Acquisition
Bibliographic record
Abstract
In this paper several detector schemes for processing weak GPS signals in an unaided acquisition scenario are described and analyzed. Fundamental theoretical considerations based on the generalized likelihood ratio test (GLRT), as applied to GPS signal detection, are discussed. It is shown that the asymptotic version of the GLRT is equivalent to an estimator correlator (EC). For assumed deterministic signals the GLRT further reduces to a matched filter. This implies that the navigation data code phase and carrier parameters are known. In this paper, we unify the well-known post-correlation noncoherent detection and the newly proposed postcorrelation differential detection in terms of GLRT and EC formulation. As well the generalized post-correlation differential detector scheme, which is a hybrid of postcorrelation, non-coherent integration and differential detectors is analyzed. Simulation results, as well as hardware based experimental measurements, are given to validate the claims of the acquisition sensitivity improvements of the proposed detection scheme.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".