A Scheme on Indoor Tracking of Ship Dynamic Positioning Based on Distributed Multi-Sensor Data Fusion
Bibliographic record
Abstract
Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable, and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference GPS, and ultrasonic sensors. Other important factors, including the indoor temperature, position, and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method, and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".