Distributed Scalable Multi-Target Tracking with a Wireless Sensor Network
Why this work is in the frame
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Bibliographic record
Abstract
We propose a novel technique for tracking multiple co-dependently maneuvering targets using a wireless sensor network. We consider the scenario where the targets carry radio frequency identification (RFID) tags and the sensors in the network measure some metric of the radio transmissions from these tags, like the received signal strength, the time of arrival or the angle of arrival. These measurements are then processed by a sampling importance re-sampling particle filter for tracking. While such a set-up is now fairly standard in literature, the novel aspect of our algorithm is that it exploits the co-dependencies in the motion of the targets via a fully distributed and tractable particle filter bank. We thereby extract a significant "diversity gain", while allowing the network to scale seamlessly to a large tracking region. In particular, we avoid the pitfalls of network congestion and severely shortened battery lifetimes that plague currently used procedures that implement the filter on the joint multi-target probability density.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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