Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks
Bibliographic record
Abstract
Efficient use of the network’s resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network’s success in tracking targets, efficiently and effectively, hinges significantly on the network’s ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multi-sensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".