Magnetic Dipole Two-Point Tensor Positioning Based on Magnetic Moment Constraints
Bibliographic record
Abstract
Magnetic target positioning methods that use magnetic gradient tensors have wide application prospects in unexploded ordnance detection, moving magnetic target tracking, and so on. However, the commonly used positioning methods, such as Nara, Frahm, and scalar triangulation and ranging (STAR), still have some problems. Namely, these methods cannot avoid the influence of the geomagnetic field, depend highly on sensor accuracy, and have poor tolerances to environmental noise, all of which severely restrict their practical applications. To overcome the aforementioned bottleneck, this article proposes a new two-point tensor positioning (TPTP) method based on a magnetic moment constraint. First, a two-point magnetic gradient tensor-measurement structure is built, and a target positioning function is constructed in which a penalty term with target magnetic moment information is introduced. Second, through simulation and comparative analysis, the approximate value range of the optimal penalty coefficient is delimited, and the objective function with penalty items greatly improves the optimization success rate. Finally, we compare the TPTP with state-of-the-art methods in various scenarios, including cases with and without geomagnetic fields, with different sensor accuracies, and with different levels of environmental noise. The experimental results indicate that the proposed TPTP method can effectively avoid the influence of the geomagnetic field. This method can also be used to realize the positioning and tracking of a magnetic target, even if the sensor accuracy is relatively low or the environmental noise is relatively large.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".