Federated Filtering Algorithm of Heading Angle Based on Magnetometer/GPS/IMU Integrated Navigation System
Bibliographic record
Abstract
In this paper, a federated filtering algorithm of heading angle based on Magnetometer (short for Mag)/GPS/IMU integrated navigation system has been designed. First of all, the advantage of Mag/GPS/IMU integrated navigation system and the main research contents of this paper have been introduced. Then the basic knowledge of federated filter has been introduced. At the third part, the algorithm of magnetic heading angle and the algorithm of combined heading angle of GPS/IMU integrated navigation system have been introduced. At the next part, EKF (Extended Kalman Filter) algorithm of Mag/GPS/IMU integrated navigation system has been presented to the reader in the forth part. Then federated filtering algorithm of Mag/GPS/IMU integrated navigation system has been introduced in the fifth part. The final part is to sum up the entire article. The paper focused on the subsystems of magnetometer navigation system and the subsystem of GPS, with IMU as the reference system. The federated filter consists of two sub-filters and the main filter. One sub-filter is the filter for magnetometer/IMU. In this sub-filter, the observed value is the heading angle difference between Mag and IMU. We can get the first state estimation matrix and its covariance matrix from the first sub-filter by EKF. The other sub-filter is the GPS/IMU filter. In this filter, the observed value is the heading angle difference between GPS and IMU. We can also get the second state estimation matrix and its covariance matrix from the second sub-filter by EKF. Then the time is updated and the output of sub-filters is integrated by the main filter. The globally optimal estimation of the heading angle of Mag/GPS/IMU integrated navigation system can be got by the main filter, and then feed back to the sub-filters according to the principle of information allocation. In this research, a vehicle experiment has been done. The experimental equipment is TPIM-10 got from Trusted Positioning Inc., Canada. This equipment is a kind of medium accuracy integrated navigation system. The calculating result of vehicle experiment data and the comparison of several algorithms show that the federated filtering algorithm is better than others. The accuracy of heading angle increased from 0.5932 deg of only Mag/GPS system to 0.1682 deg, while the algorithm can inhibit IMU error accumulation effectively. The fault tolerance and stability of federated filtering algorithm have been showed in this paper. Mag/GPS/IMU integrated navigation system is not only suitable for high and low dynamic, but also for static state. Moreover, the data processing by federated filtering algorithm can significantly improve the performance of the navigation system.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.002 |
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".