Minimum error entropy iterative extended Kalman filter for robust GPS/IMU/visual-integrated navigation under GPS anomalies
Bibliographic record
Abstract
The integrated navigation of global satellite navigation system (GNSS) and inertial measurement unit (IMU) faces the problems of satellite signal limitation and IMU error accumulation in complex environments such as forests and long tunnels. Adding visual sensors is a good solution. In the majority of existing researches, non-Gaussian ambient noise is mostly regarded as Gaussian noise to simplify the processing, how to deal with non-Gaussian ambient noise remains a burning issue. In this paper, a minimum error entropy (MEE) based iterative extended Kalman filtering (IEKF) method for GPS/IMU/Visual-integrated navigation is developed. First, IEKF is employed to process GPS abnormal data and gradually approach the real state through an iterative optimization. Second, the MEE criterion is combined to enhance the robustness of the system to non-Gaussian noise, and the fixed-point iteration method is used to calculate the state estimation to improve the positioning accuracy of the system. Finally, the Canadian and Katwijk navigation data sets are utilized to validate the effectiveness of the proposed method. The results show that compared with the traditional EKF-based method, the proposed method can reduce the longitude error by 5.9% and 29.2%, and the latitude error by 19.3% and 56.6%, respectively, in GPS abnormal and non-Gaussian noise environments, and the accuracy and robustness of the navigation system are significantly improved.
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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".