Integrated recursive least square lattice and neuro-fuzzy modules for mobile multi-sensor data fusion
Bibliographic record
Abstract
The last two decades have witnessed an increasing trend in integrating different navigation systems to overcome the limitations of the stand-alone operation of such systems. For instance, GPS is usually combined with Inertial Navigation System (INS) in several navigation applications. Most of the INS/GPS integration techniques relied on Kalman filtering (KF). Recently, artificial intelligence based techniques were also introduced to replace KF. In order to avoid some of the limitations of the present techniques, this paper introduces multi-sensor systems integration using Recursive Least Square Lattice (RLSL) filter along with an artificial intelligence technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed technique was examined with field test data conducted in a land vehicle using a tactical grade INS (Honeywell HG1700) integrated with Differential GPS measurements collected by a NovAtel OEM4 GPS receiver. The results indicate that the proposed RLSL/neuro-fuzzy system is robust in providing a reliable real-time INS/GPS integration module.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".