Localization technology of underwater acoustic wireless network based on array signal processing
Bibliographic record
Abstract
In the area of large wireless sensor networks (WSNs), localization is important for many applications. In the WSNs, some nodes can localize themselves, which are called anchor nodes. A traditional method called three dimensional underwater localization (3DUL) is to deploy buoys equipped with GPS receivers on the surface of the sea. Sensors can estimate their distances to the anchors using the location information of anchors. This method is quite straightforward but not economical for large-scale WSNs, suffering from limitations such as time consumption and low coverage. In certain circumstances, this method becomes infeasible. In order to overcome the above-mentioned problems involved in the fixedreference method, localization systems using mobile anchors have been developed. In the underwater environment, autonomous underwater vehicles (AUVs) are good choices of mobile anchors. An AUV can be set to move along a predefined trajectory and broadcast its location information to underwater sensor nodes through acoustic signals. After receiving the location information, sensor nodes can localize themselves. The key advantage of this method is that the costs for deploying such sensor networks remain relatively low, even though a large number of anchors is used or the area of interest are extended. A hydrophone is a device that can receive underwater acoustic signals. In the application of underwater localization, multiple hydrophones can be arranged in an array to improve localization accuracy. Hydrophone can be divided into three types: (1) long baseline system (LBL), (2) short baseline system (SBL), (3) ultra short baseline system (USBL). Furthermore, a hydrophone array can be loaded on an AUV to create a big virtual array, which is the principle of synthetic aperture sonar (SAS). The advantage of SAS is that it can optimize the tradeoffs between sonar array length and range scale.Motivated by these ideas, this thesis work has investigated AUV-aided localization systems using three kinds of hydrophone arrays: (1) a Doppler localization system with a single hydrophone, (2) a towed uniform linear array (ULA) based localization system with a new structure, (3) underwater localization system assisted by a moving uniform circular array (UCA). Detailed challenges in underwater localization and solutions to them are presented. System models are established and verified based on simulation results. It has been demonstrated that localization accuracy can be improved dramatically by using hydrophone arrays. In the first application, Doppler-based localization method has been found to perform better than time of arrival (ToA) and time difference of arrival (TDoA) methods. In order to increase localization accuracy, a new structure of towed sonar array has been proposed and it can be useful for localizing an underwater object. Associated with improved localization accuracy, the computational complexity can be lowered. Because UCA gives a 360○ azimuth coverage, it can be mounted on an AUV to achieve the task of localizing an underwater object. The accuracy of this method can be higher than the ULA method, but the computational complexity increases as well.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".