Development of Intelligent GNSS-based Land Vehicle Localisation Systems
Bibliographic record
Abstract
The usage of Global Navigation Satellites Systems (GNSS) for localisation purposes demands a permanent evaluation of the position information provided for the receiver, as well as a standardised GNSS-Receivers validation methodology and subsequently quality control procedures oriented to land vehicles within the ergodic hypothesis. The use of an independent reference system should provide enough information to validate the localisation system, but the lack of proper evaluation and procedures presents significant blind spots for future applications in both the GNSS-Receiver and the correspondent reference system. To solve these problems an approach based on artificial intelligence (AI) is presented. Also the development of an advanced filter technique for positioning estimation results in significant improvements of the reference system, even allowing a standalone GNSSdependent reference system when no independent systems are available. The presented developments are the bases for future intelligent GNSS-based localisation systems. The methodologies combine the advanced Particle Filter (PF) for positioning estimation with the newly developed Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation and the Artificial Neural Networks (ANN) models for both quantitative and qualitative validation. In this thesis the bases of the intelligent GNSS-based localisation system are presented and developed follows the BMW principle. In German the BMW principle stands for Beschreibungsmittel (means of description), Methode (methods) and Werkzeug (tool). The resulting system described along the thesis is applied and tested in a demonstrator tool, validating the developed methodologies in both software and hardware level. The proposed methodologies for the development of an intelligent GNSS-based localisation system are a substantial contribution for intelligent GNSS-based validation tools that will enable future safety-relevant applications, in field such as on-board uncertainty evaluation of vehicle localisation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective enabled-localisation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.003 |
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".