Least‐squares‐based adaptive target localization by mobile distance measurement sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary A least‐squares‐based adaptive algorithm with forgetting factor is proposed for localization of a target by a mobile distance measurement sensor. This problem, in its most general form, was tackled in a recent paper using a gradient adaptive algorithm, assuming distance measurements are directly available. We establish that the proposed algorithm bears the same stability and convergence properties as the gradient algorithm previously studied. It is demonstrated via simulations that the proposed algorithm converges significantly faster to the location estimates than the gradient algorithm for high forgetting factor values and significantly reduces the noise effects for small values of the forgetting factor. Furthermore, a more challenging form of the original problem is considered, where distance information is required to be deduced from time of flight measurements, considering a time of flight‐based active distance measurement sensor and an environment with unknown signal permittivity/speed; the proposed algorithm is redesigned to solve this problem. Copyright © 2014 John Wiley & Sons, Ltd.
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.
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 | 0.000 | 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 it