Registration in a distributed multi-sensor environment
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
To gain any benefit from a network of multiple sensors, it is essential that each sensor be correctly integrated into a global frame of reference. This process is termed REGISTRATION. Any error in the global coordinate system introduced through the sensor reports or the nature of the coordinate system itself has the potential to introduce sufficient ambiguity to compromise the utility of the multi-sensor network. Techniques for the solution of registration among two sensors are well established in the literature, but when the network involves a larger number of sensors (N sensor case), techniques for 2 sensor registration cannot be efficiently adapted because of their algorithmic growth rates, insufficient sensor visibility, dissimilar sensor types and the resulting multi modal cost functions. This paper examines sensor registration techniques adapted for networks with a large number of sensors using traditional least square estimation and non traditional evolutionary computation in the context of registering a netted radar system. The analysis is supported by extensive simulation including recorded radar data from a network of distributed radar sensors.
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