Multi-sensor Attitude and Heading Reference System using Genetically Optimized Kalman Filter
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
An Attitude and Heading Reference System (AHRS) comprising accelerometers, gyroscopes and magnetometers can provide roll, pitch and heading information. AHRS is utilized in many applications such as navigation, augmented/virtual reality, and mobile mapping. The AHRS mechanization involves integration of angular rate measurement to provide high rate orientation but with unbounded drifts due to accumulation of random noise. To reduce drifts, mechanization output is combined with absolute measurement from magnetometer and accelerometer using Extended Kalman Filter(EKF). EKF accuracy is greatly affected by process covariance matrix (Q) and measurement noise covariance matrix(R). Conventional stochastic modeling approaches to determine Q and R parameters do not guarantee best performance. This paper proposes a systematic procedure for EKF parameters optimization using a hybrid statistical and genetic algorithms (GA) approach. The proposed approach has been verified on real data collected by an inertial measurement unit. Results showed that the Q and R can be optimized within few GA iterations outperforming conventional EKF parameter estimation methods.
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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