Mixture Particle Filter for Low Cost INS/Odometer/GPS Integration in Land Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global Positioning System (GPS) is currently the common solution for land vehicle positioning. However, GPS signals may suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution to have continuous vehicle positioning is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To achieve a better performance during GPS outages, the speed derived from the vehicle odometer is used as a measurement update. To improve the positioning accuracy of the MEMS-based INS/Odometer/GPS integration, particle filtering (PF) is used as a nonlinear filtering technique, which does not need to linearize the models as in Extended KF (EKF). Because of PF ability to deal with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. An enhanced version of PF is used which is called Mixture PF. While the Sampling/Importance Resampling (SIR) PF samples from the prior importance density and the Likelihood PF samples from the observation likelihood, the Mixture PF samples from both densities, then appropriate weighting is achieved followed by resampling. This mixture of importance densities leads to a better performance. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it