Continuous Urban Navigation with Next-Generation, Mass Market Navigation Sensors and Adaptive Filtering
Bibliographic record
Abstract
The Global Navigation Satellite System (GNSS) Precise Point positioning (PPP) technique benefits from not needing local ground infrastructure such as reference stations and accuracy attained is at the decimetre-level, which approaches real-time-kinematic (RTK) performance. However, due to its long position solution initialization period and dependence on the receiver measurements, PPP finds limited utility in obstructed areas. The emergence of low-cost, high-performance micro-electromechanical sensor (MEMS) inertial measurement units (IMUs) has prompted research in integrated navigation solutions with GNSS PPP augmentation. In this study, novel research is performed using a low-cost, dual- and triple-frequency (DF and TF) GNSS and, MEMS IMU to attain decimetre to sub-metre accuracy in challenging environments. New-generation applications demand decimetre-level positional accuracy while using low-cost equipment. PPP that does not need any local infrastructure has become a promising technique to be used for such mass-market applications. The objectives of the research are to examine the effect of sensor constraining to improve position accuracy, assess the performance of TF PPP and MEMS IMU algorithm in open-sky and simulated outages, and use adaptive filtering to maintain decimetre to sub-metre-level accuracy in all environments using low-cost sensors. An uncombined GNSS PPP solution was integrated with MEMS IMU using tightly-coupled architecture. The novelty addressed by this research is the combination of the low-cost hardware and the software constraining that is used together to provide significantly improved continuous and accurate navigation in the urban environment, which has not been examined previously in the PPP + IMU research area. Furthermore, to achieve sub-metre level horizontal accuracy, modification to the traditional robust adaptive Kalman filter (RAKF) is proposed. Data collected in open sky, as well as urban environments, were assessed for the performance in simulated as well as real urban outages. The integrated system performs with less than a decimetre-level accuracy in open-sky and sub-metre-level in simulated environment. Sub-metre-level rms results were attained by using the novel modified RAKF in urban areas. The outcomes of this research are reassuring towards achieving continuous navigation solutions with decimetre to sub-metre level accuracy for modern applications that demand higher accuracy in all environments while using low-cost equipment.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".