UNIVERSITY OF CALGARY Modeling Inertial Sensors Errors Using Allan Variance
Bibliographic record
Abstract
It is well known that Inertial Navigation Systems (INS) can provide high accuracy information on the position, velocity, and attitude over a short time period. However, their accuracy degrades rapidly with time. The requirements for accurate estimation of navigation information necessitate the modeling of the sensors ’ noise components. Several methods have been devised for stochastic modeling of inertial sensors noise. Each of them is useful but each has its own disadvantage. The Adaptive Kalman filter is one of the mostly used methods to estimate inertial sensor noise, but the form of the model needs to be known first. Frequency domain approaching uses the power spectral density to estimate transfer functions. It is straightforward but it is difficult for non-system analysts to understand. In the time domain methods, the correlation function approach is very model sensitive. Variance techniques are basically very similar, and primarily differ only in that various signal processing, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest technique is the Allan variance method. Allan variance is a method of representing root mean square (RMS) random drift error as a function of average time. It is simple to compute and relatively simple to interpret and understand. Allan variance method can be used to determine the character of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of noise terms in the inertial sensor data by performing certain operations on the entire length of data. In this thesis, the Allan variance technique is used in noise analysis of different grade
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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".