Design and Testing of an Intelligent GPS Tracking Loop for Noise Reduction and High Dynamics Applications
Bibliographic record
Abstract
Autonomous Navigation Systems (ANS) used in ballistic or cruise missiles are mostly dependent on Global Positioning System (GPS) as a primary mean of navigation. GPS usage has limitations in terms of missile high dynamics and signal interference. The GPS receiver requirements to avoid these problems are conflicting. The Phase Lock Loops (PLLs), used to track GPS signals, are required to have a bandwidth as narrow as possible to reduce the impact of signal interference. On the contrary, the loop bandwidth has to be as wide as possible to accommodate high signal dynamics. The approach usually adopted in such cases is to use a Frequency Lock Loop (FLL) to reduce frequency acquisition/reacquisition times before switching back to PLL or FLL-assisted-PLL. This approach does not prevent frequent loss of lock, compromising the receiver performance. In this paper, a novel FLL-assisted-PLL is proposed for very high dynamic conditions with reduced measurement noise. The design is based on fuzzy control systems, and is used to directly generate the required Numerical Control Oscillator (NCO) tuning frequency using phase and frequency discriminators information. The designed system is compared against 3rd order PLLs, with narrow and wide bandwidths, in addition to a standard FLL-assisted-PLL. Simulation results show the enhanced performance of the proposed system where better tracking continuity and less noisy measurements are achieved.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".