Distributed target tracking using signal strength measurements by a wireless sensor network
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
Wireless Sensor Networks are well suited for tracking targets carrying RFID tags in indoor environments. Tracking based on the received signal strength indication (RSSI) is by far the cheapest and simplest option, but suffers from secular biases due to effects of multi-path, occlusions and decalibration, as well as large unbiased errors due to measurement noise. We propose a novel algorithm that solves these problems in a distributed, scalable and power-efficient manner. Firstly, our proposal includes a tandem incremental estimator that learns and tracks the radio environment of the network, and provides this knowledge for the use of the tracking algorithm, which eliminates the secular biases due to radio occlusions etc. Secondly, we reduce the unbiased tracking error by exploiting the co-dependencies in the motion of several targets (as in crowds or herds) via a fully distributed and tractable particle filter. We thereby extract a significant 'diversity gain' while still allowing the network to scale seamlessly to a large tracking area. In particular, we avoid the pitfalls of network congestion and severely shortened battery lifetimes that plague procedures based 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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