New Developments in State Estimation for INS/GPS Integrated Systems
Bibliographic record
Abstract
This paper proposes a new filter for INS/GPS integration based on a new interpolation formula known as Discrete Singular Convolution (DSC)-based Generalized Finite Difference. Singular convolutions are essential to many science and engineering problems such as stochastic process analysis. By appropriate approximation of a singular kernel in DSC scheme, the discrete singular convolution can be an extremely efficient, accurate and reliable algorithm for practical applications. The theory of distribution and wavelet analysis form the mathematical foundation of DSC. The objective is to explore the utility of the DSC algorithm for the development of a new filter for INS/GPS integration system. The significance of this paper is that the higher order DSC-based finite difference approximation can be considered by implicitly calculating the DSC-based first and second partial differentiations (Jacobian and Hessian approximation) involved in the second order modified Gaussian Kalman filter (SOKF) scheme. To examine the performance of the proposed filter, dual frequency GPS/INS data are collected onboard a hydrographic surveying vessel owned by the Canadian Hydrographic Service (CHS). The unscented Kalman filter (UKF) is also examined and compared with the developed DSC-based SOKF. It is shown that the accuracy (RMS error) of the developed DSC-based SOEKF state estimator is better than the UKF estimator.
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.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".