EM-IMM based land-vehicle navigation with GPS/INS
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
Integration of the global positioning system (GPS) with the inertial navigation system (INS) is favorable since it provides enhanced positioning accuracy. Its implementation is essentially based on the standard Kalman filter techniques. However, the estimation accuracy is degraded if unknown parameters present in the system model or the model changes with the environment as in the case of intelligent transportation systems (ITS). We propose an expectation-maximization (EM) based interacting multiple model (IMM) method, namely, EM-IMM algorithm, to jointly identify the unknown parameters and to estimate the position information. The IMM is capable of identifying states in jumping dynamic models corresponding to various vehicle driving status, while the EM algorithm is employed to give the maximum likelihood (ML) estimates of the unknown parameters. Compared to the conventional single model Kalman filter based navigation, the proposed algorithm gives improved estimation performance when the land-vehicle drives with changing conditions. Simulation results demonstrate the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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