Bibliographic record
Abstract
This paper presents the technologies available to locate the origin of radio frequency transmissions for Personnel Location Systems. The development and application of these systems will be discussed. A new class of signal strength location systems will be introduced that sets the standard for locating accuracy in indoor and campus environments. Many technologies have been applied to locating radio signals. Early radio frequency location systems used mechanically-rotated directional antennas or pseudo-Doppler array techniques. They were developed for outdoor use over relatively long ranges. The reliability and accuracy fall short of today's personnel location requirements that demand both indoor and outdoor coverage of large building campuses. Ubiquitous Global Positioning System (G.P.S.) service delivers reliable radio frequency location in outdoor areas. Differential G.P.S. provides sufficient accuracy for most outdoor personnel tracking applications. Recent cost reductions in OEM-grade G.P.S. receiver printed circuit assemblies have made G.P.S. solutions economically feasible for outdoor personnel location. Bi-directional LEOs-based satellite location systems are under development that also can be applied to personnel location. Various users have attempted to locate transmitters using relative time of arrival (TOA) algorithms, with mixed results. Ultrawideband (UWB) technology, offers the potential for accurate RF location indoors and out but FCC concerns with potential interference to GPS services are preventing approval. The most accurate radio frequency location systems operational within the security industry today employ relative signal strength to calculate position. Recent advances in signal strength modeling software and search algorithms have extended the accuracy of signal strength locating systems. These advances have been incorporated into the FLARE/sup TM/ system developed by Dominion Wireless. Originally designed to meet specifications developed by Correctional Service Canada, the FLARE signal strength locating system has now been adopted by other corrections authorities in the United States and Canada. The theory of operation of FLARE will be presented. Current implementations will be discussed and the capabilities and limitations of actual deployed systems will be reviewed. Finally, the future plans for the refinement and advancement of FLARE will be presented and projections of enhanced performance will be supplied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.037 |
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".