High-order pseudorange rate measurement model for multi-constellation LEO/INS integration: Case of Iridium-NEXT, Orbcomm, and Globalstar
Bibliographic record
Abstract
An inertial navigation method augmented by Signals of Opportunity (SOPs) of three low earth orbit (LEO) constellations is presented. The downlink signal characteristics of the Iridium-NEXT, Orbcomm, and Globalstar LEO constellations are discussed. Furthermore, a tightly coupled integration model of the inertial navigation system and high-order LEO-SOP Doppler measurement model is designed. We presented a second-order measurement model of the LEO-SOP/INS integration using a second-order extended Kalman filter in which all the unknown states of the receiver and LEO satellites are estimated. The state parameters of the second-order EKF model are the position and velocity of both the receiver and the satellites, as well as the receiver’s orientation, the clock bias, and clock drift of the LEO satellites, and the constant bias of the Inertial Measurement Unit. An experiment is performed using a ground aerial vehicle equipped with a Multi-Constellation Software-Defined Receiver (MC-SDR). The Doppler measurements are provided by observing the downlinks from multiple satellites of the Iridium-NEXT and Orbcomm constellations. As well, the predicted measurement of a Globalstar satellite is used in the designed model. The results show the positioning accuracy of less than 10 m being achieved during a dynamic ground experiment, representing an 82% precision gain as compared against the regular single constellation EKF method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".