Multi-Sensor Attitude and Heading Reference System Design Using Genetically Optimized Kalman Filter
Bibliographic record
Abstract
Attitude and Heading Reference System (AHRS) is a self-contained sensors assembly that can estimate full 3D orientation of an object. 3D orientation estimation is of great importance in many applications such as robot navigation, augmented/virtual reality, and mobile mapping. The AHRS system model involves integration of angular rate measurements from gyroscope which are fused with absolute measurements from magnetometer/accelerometer using Extended Kalman Filter (EKF). EKF accuracy is greatly affected by process noise parameters and measurement noise parameters. Therefore, this thesis developed a systematic method of EKF noise parameters optimization using a hybrid stochastic, Genetic Algorithms (GA)-based approach supported by Design of Experiments (DoE) technique. Noise parameters are partially obtained using stochastic modeling methods such as Gauss-Markov (GM) and Allan Variance (AV). Then, further optimization is performed using GA and DoE methods. The proposed approach has been developed in MATLAB and tested on simulation data and verified on real data collected under different scenarios. Results showed that the proposed approach can provide 40-60% better accuracy compared to conventional methods within few GA iterations. In addition, application of DoE technique reduces GA iterations to convergence by approximately 60%.
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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".