Dynamic sensor management for distributed tracking
Bibliographic record
Abstract
In this paper, we consider the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework. As a result of recent technological advances, a large number of sensors can be deployed and used for tracking purposes. However, only a certain of number of sensors can be used by each local fusion center due to physical limitations. In addition, the number of available frequency channels is also limited. We can expect that the transmission power of the future sensors will be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers, in order to maximize the tracking accuracies as well as to minimize the total power consumption. This is an NP-hard multi-objective mixed-integer optimization problem. In the literature, sensors are clustered based on target or geographic location, and then sensor subsets are selected from those clusters. However, if the total number of LFCs is fixed and the total number of targets varies or a sensor can detect multiple targets, target based clustering is not desirable. Similarly, if targets occupy a small part of the surveillance region, location based clustering is also not optimal. In addition, the frequency channel limitation and the advantage of the variable transmitting power are not discussed well in the literature. In this paper, we give the mathematical formulation of the above problem. Then, we present an algorithm to find a near optimal solution to the above problem in real time. Simulation results illustrating the performance of the sensor array manager are also presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".