Alternative PNT based on Iridium Next LEO Satellites Doppler/INS Integrated Navigation System
Bibliographic record
Abstract
This paper addresses an original problem of integrated navigation system based on iridium Next low Earth orbit Iridium Next satellites. In uncovered Radar areas such as oceanic regions, in the northern, southern poles, or in the desert regions, it is important that any aircraft can navigate even in denied GNSS environment. In such conditions, how to maintain tracking information of airlines especially during distress and emergency situations? to achieve that, a new design Inertial/Doppler integration design is developed and proposed. Position and speed of the aircraft are estimated based on multiple Doppler information fusion from Low Earth Orbit (LEO) satellites downlink signals. In this paper, Iridium Next LEO constellation is considered as an emerging technology, and privileged for search and rescue and flight safety applications. Simulations based on experimental data collected by USRP E310 demonstrated very good performances. The new navigation system represents a good alternative to GNSS Positioning, Navigation and Timing Solution (PNT). To achieve high performances, derivative free distributed nonlinear filtering algorithms based on multi variant Quadrature Kalman filters are considered and implemented. A distributed design centralized at the Iridium gateway carried out very good results to be considered as an extended solution to Radar information used to track airlines and manage airspace by its integration into the Air Traffic Management System (ATMS) in all countries.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".