Automization of an INS/GPS intecrated system using genetic optimization
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
Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model, immunity to noise effects and observability. Most recently, an INS/GPS integration method using a hybrid-adaptive-neuro-fuzzy integration system (ANFIS) has been proposed by the authors. The advantage of the ANFIS over other classical filtering algorithms is its ability to deal with noise in the input data in dynamic environments. During the availability of GPS signal, the ANFlS is trained to map the error between the GPS and the INS. The ANFIS will then be employed to predict the error of the INS position components during GPS signal blockage. As ANFIS will be used in real time applications, the change in the system parameters (e.g. the number of membership functions, the step size, arid step increase and decrease rates) to achieve the minimum training error during cach time period is automated. This paper introduces a genetic optimization algorithm that is used to update the ANFlS parameters with the INS/GPS error function used as the objective function to be minimized. Challenges encountered in the integration process are discussed and the proposed architecture is tested in a land vehicle navigation. GPS signal outage of a time period of 120 seconds was simulated during 1420 seconds of land vehicle navigation. The experimental results demonstrated the advantages of the genetically optimized ANFlS For lNS/GPS Integration.
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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 | 0.001 |
| 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