Algorithm Design and Performance Analysis of Target Localization Using Mobile Underwater Acoustic Array Networks
Bibliographic record
Abstract
Space-air-ground-sea integrated network, as a promising networking paradigm for the sixth generation (6 G) communications, connects satellite networks, aerial networks, terrestrial networks, and marine networks. As a fundamental application of marine networks, efficient target localization in the ocean is significant for many marine and underwater applications, including oceanic environmental monitoring, subsea resource exploration, and navigation safety. In this paper, to bring more scalability and feasibility of underwater localization, a mobile underwater acoustic array network is utilized to locate underwater moving targets by leveraging linear frequency modulated (LFM) signals. In the mobile underwater acoustic network, one node is constantly broadcasting LFM signals. Based on the reflected signals received by other nodes, a method that jointly utilizes the propagation delay and the Doppler effect is proposed to simultaneously estimate the position and the velocity of the moving target. Specifically, a two-phase iterative algorithm with low computational complexity is designed to improve the estimation accuracy. Closed-form expressions of positioning and velocity estimation error are also presented. Performance evaluation shows that the proposed method clearly outperforms the least squares based approach. Moreover, the estimation accuracy of the proposed method can approach the Cramér-Rao low bound (CRLB) within two iterations. Decently good localization and velocity estimation error performance can be achieved even with an array network formed by a small number of nodes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".