Pedestrian Trajectory Projection Based on Adaptive Interpolation Factor Linear Interpolation Quaternion Attitude Estimation Method
Bibliographic record
Abstract
In recent years, with the continuous development of the Internet of Things (IoT) technology, smart devices, such as smart homes and smart phones, have been widely used, so the demand for providing specific location services has gradually increased, while the traditional positioning service technology based on satellite information is difficult to provide reliable accuracy in indoor environments due to various constraints; meanwhile, because of the specific conditions of use, positioning methods requiring the presetting of auxiliary equipment will be ineffective. In order to solve these problems, autonomous indoor positioning technology using only a single sensor has an irreplaceable role. This article takes a low-cost, high-precision indoor positioning technique based on step heading style using only a single magnetic angular rate and gravity (MARG) sensor and proposes an interpolation factor-adaptive quaternionic attitude solving algorithm based on linear interpolation (LERP). The method uses a motion state metric matrix for describing the intensity of the current motion state and calculates the interpolation factor adaptively using the motion state metric matrix. Based on the characteristic that different motion states have different optimal interpolation factors, the method adopts the adaptive updating calculation method to automatically update the interpolation factors, which gets rid of the problems that may arise from the pregiven interpolation factors and extends the scope of the method, and the corresponding heading angle calculation can be carried out for all the motion states, and finally, by combining the proposed heading angle estimation method with the high-precision step segmentation method and step length estimation method, a step-length heading-based positioning method is proposed, which achieves an average positioning accuracy of 0.3825 m. Moreover, only one MARG sensor fixed at the waist is used, and the cost of the device is very low at only U.S. 14.9, which satisfies the requirements of high accuracy and low cost at the same time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".