A rapid deployment guided radar sensor
Bibliographic record
Abstract
A need has long existed for a rapid deployment, terrain following security sensor for use around portable resources, along an avenue of approach, or for the temporary replacement of a failed sensor. Existing solutions such as portable microwave or passive infra-red (PIR) sensors are relatively inexpensive, but cannot work over uneven terrain, around corners, or in foliage. The cost and installation complexity of these sensors increases rapidly as more units are required. The RepelsregRF sensor provides many of the required features, but uses sensor cables that are overtly mounted above ground. The OmniTraxregtechnology was first introduced at the 2004 Carnahan Conference, applying ultra wide band radar principles to a ranging leaky cable guided radar sensor. In 2007, a program called TFDIDS (terrain following deployable intrusion detection sensor) was initiated in conjunction with the US Air Force, to apply the advancements in this ranging technology to the rapid deployment sensor needs defined by the USAF tactical automated sensor system (TASS). This advancement includes a novel invention employing the processing of dual parallel leaky sensor cables, termed Stereo OmniTrax. This processing dramatically improves the discrimination between human intrusion threats and small nuisance targets or environmental effects. The TFDIDS system provides a complete lightweight sensor kit for the rapid deployment (less than 30 minutes) of a 100 m detection zone, and later, for the sensor's retrieval and reuse. TFDIDS interfaces to standard Government Furnished Equipment (GFE) including USAF powering and communications devices. This paper outlines the key elements of the TFDIDS design, describes its components, and explains how TFDIDS provides reliable detection using a surface sensor cable deployment. Initial performance results are presented, from tests conducted at the Senstar SITE in 2008. The test applications include through-the-woods, on tarmac and on typical open field surfaces.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".