A Novel Architecture for Ultra-Tight HSGPS-INS Integration
Bibliographic record
Abstract
Global Positioning System (GPS) currently fulfills the positioning requirements of many applications under Line-Of-Sight (LOS) environments. However, many Location-Based Services (LBS) and navigation applications such as vehicular navigation and personal location require positioning capabilities in environments where LOS is not readily available, e.g., urban areas, indoors and dense forests. Such environments either block the signals completely or attenuate them to a power level that is 10-30 dB lower than the nominal signal power. This renders it impractical for a standard GPS receiver to acquire and maintain signal tracking, which causes discontinuous positioning in such environments. In order to address the issue of GPS tracking and positioning in degraded signal environments, a novel architecture for ultra-tight integration of a High Sensitivity GPS (HSGPS) receiver with an inertial navigation system (INS) is proposed herein. By enhancing receiver signal tracking loops through the use of optimal estimators and with external aiding, the capabilities of the receiver can be substantially improved. The proposed approach is distinct from the commonly used ultra-tightly coupled GPS/INS approaches and makes use of different tracking enhancement technologies used in typical HSGPS receivers, multi-channel cooperated receivers and the current ultra-tightly coupled GPS/INS methods. Furthermore, the effects of inertial measurement unit (IMU) quality, receiver oscillator noise and coherent integration time on weak signal tracking are also analyzed. Simulated test results in both static and dynamic testes show that, the designed INS-aided GPS receiver can track the incoming weak GPS signals down to 15 dB-Hz without carrier phase locked, or 25 dB-Hz with carrier phase locked. When there are multiple strong GPS signals in view, the other weak signals can be tracked down to 15 dB-Hz with carrier phase locked.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".