Multiple ultrasonic aiding system for car navigation in GNSS denied environment
Bibliographic record
Abstract
This paper proposes a novel approach for estimating the navigation states of land vehicles in GNSS denied environment by integrating low-cost multiple ultrasonic sensors with the Inertial Measurement Unit (IMU) using Extended Kalman Filter (EKF). These multiple ultrasonic sensors act as an aiding by providing the vehicle forward velocity to limit the INS large drift during GNSS signal outages. Ultrasonic sensors are installed on the left and right rear wheels to measure the range difference between the sensor and the spokes of the wheel to determine the angular velocity and then determine the vehicle forward velocity. As the ultrasonic raw data is contaminated with outliers and noise, outliers' removal is applied, and a moving average filter is used to reduce the noise. Two experimental road tests were performed for low velocity (30 km/hr) and moderate vehicle velocity (50 km/hr). Ultrasonic sensors were integrated with GNSS/INS in loosely coupled integration scheme through EKF. The Root Mean Square Error (RMSE) of the velocity estimated by the ultrasonic sensors was 0.28 m/sec. Moreover, the position RMSE enhanced from 101.18 meters for the case of INS standalone navigation solution to 5.07 meters when INS integrated with ultrasonic sensors for GNSS signal outage of 60 seconds in the first test. The RMSE of the position is decreased to 17.99 meters in case of ultrasonic/INS integration navigation solution compared to INS standalone solution with RMSE of 72.43 meters for an outage of 60 seconds in the second test. The proposed multiple ultrasonic system provides the land vehicle navigation solution with forward velocity update with higher accuracy and data rate than the velocity provided by regular odometer of On-Board Diagnostics (OBD II).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".