Multi-UUV object detection, localization and tracking with secure, full-duplex communication networks
Bibliographic record
Abstract
Proposed, is a novel OFDM/CDMA communications network that runs on full-duplex software-defined underwater acoustic modems. This system is designed to detect, localize and track mobile underwater targets using multiple collaborating unmanned underwater vehicles (UUVs). This underwater network will be presented and described as a communications system. To start, the underwater channel characteristics (shallow, multi-path and high ambient) are described. The requirement is signal propagation that is secure (encoded), spread spectrum, robust to frequency selective fading, and efficient. This requirement is not easily met by existing full-duplex communications stacks. The proposed OFDM and CDMA modulation schemes better address this on embedded systems like UUVs. Therefore, the modem’s functions are integrated into the UUV with the Robot Operating System (ROS) middleware. These acoustic modems natively run UnetStack – a stack that focuses on underwater communication networks. The ROS nodes that control the UUV communicate to/with the modem using the UnetPy’s socket API. Scenarios are demonstrated where one stationary UUV/modem and two underway ones communicate in a full-duplex configuration. In the process, messages / signals on UUV/modem location, speed, heading, etc. are securely and efficiently exchanged among UUVs/modems. Finally, the proposed system is validated through UnetStack’s hardware-in-the-loop simulations (UnetSim) and multiple in-water tank trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".