Decentralized Multi-target Tracking in Urban Environments: Overview and\n Challenges
Bibliographic record
Abstract
In multi-target tracking, sensor control involves dynamically configuring\nsensors to achieve improved tracking performance. Many of these techniques\nfocus on sensors with memoryless states (e.g., waveform adaptation, beam\nscheduling, and sensor selection), lending themselves to computationally\nefficient control strategies. Mobile sensor control for multi-target tracking,\nhowever, is significantly more challenging due to the complexity of the\nplatform state dynamics. This platform complexity necessitates high-fidelity,\nnon-myopic control strategies in order to achieve strong tracking performance\nwhile maintaining safe operation. These sensor control techniques are\nparticularly important in non-cooperative urban surveillance applications\nincluding person of interest, vehicle, and unauthorized UAV interdiction. In\nthis overview paper, we highlight the current state of the art in mobile sensor\ncontrol for multi-target tracking in urban environments. We use this\napplication to motivate the need for closer collaboration between the\ninformation fusion, tracking, and control research communities across three\nchallenge areas relevant to the urban surveillance problem.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".