Utilization of Wavelet Packet Sensor De-noising for Accurate Positioning in Intelligent Road Services
Bibliographic record
Abstract
Recently, smart cities functionality and management have captured notable consideration. Owing to the rapid development in the information and communication technologies (ICT), various applications and services are highly engaged in the cities' operation. Specifically, intelligent road services as traffic management, driver behavior assessment and crowdsensing based road condition monitoring contribute towards better operability. To sustain decent performance of these applications, accurate and continuous positioning is an essential concern. Generally, Global Navigation Satellite System (GNSS) receivers are vulnerable to partial or complete outages due to multipath or signal blockage. Consequently, inertial navigation systems integrated with GNSS receivers are affected by inertial sensors noises and biases. In this paper, we apply wavelet packet de-nosing to eliminate noises of the Micro-Electro-Mechanical Systems (MEMS) grade inertial sensors. Afterwards, we integrate the de-noised reduced inertial sensor system (RISS) with GNSS receivers in real road experiment to assess the system performance. In addition, we show the significance ofthe proposed integration over the conventional one during multiple GNSS outages under various driving scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".