GPS Integrated Inertial Navigation System Using Interactive Multiple Model Extended Kalman Filtering
Bibliographic record
Abstract
This paper presents an implementation of a Global Positioning System (GPS) integrated inertial navigation system (INS) for vehicle state estimation. The INS uses Extended Kalman Filtering (EKF) of the linearized state space model for state estimation. The two INS EKF models have differently tuned noise parameters. The models operate in parallel using an interactive multiple model (IMM) approach. The IMM mixes the state and state covariance estimates from both models to yield a combined estimate of the system states. The mixing weights are based on the likelihood of each model correctly tracking the system states. The likelihoods are computed using the innovation and innovation covariance matrices of each model. The model with the higher likelihood has a larger influence on the overall state estimation. The KITTI Vision Benchmark dataset has been utilized for testing and validation. The GPS coordinates have been transformed into a local tangent frame position estimation. Orientation measurements are provided by the dataset for heading correction. The analysis shows that the INS system accurately tracks the position and orientation; the IMM filter generally outperforms the single EFK model estimator during turning maneuvers where the IMM filter produces a lower mean position error than a single EKF filter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".