High Accuracy AUV-Aided Underwater Localization: Far-Field Information Fusion Perspective
Bibliographic record
Abstract
An autonomous underwater vehicle (AUV) can be employed to estimate an underwater target’s position using the Doppler shift measurement extracted from received signals. Conventionally, the received signals have to be divided into several short frames so that the Doppler shift is constant in each one. When the AUV is far away from the target, the signal to noise ratio (SNR) is quite low. An intuitive solution is to increase the frame length to suppress noise and boost SNR. However, it is worth noting that the Doppler shift is actually changing over time, and the prolonged frame length will inevitably induce modeling error. This is a fundamental dilemma in the Doppler-shift-based underwater localization. In this paper, we reveal that the assumption of constant Doppler shift comes from the zero-th order Taylor expansion of the real-time model. To increase the frame length without jeopardizing the model accuracy, we modify the model by taking the first-order Taylor expansion. By doing so, the frame length can be prolonged by one order of magnitude without introducing non-negligible modeling error. What this new model says is that when the target broadcasts a single-tone signal, the AUV will receive a linear frequency modulated (LFM) signal parameterized by a Doppler shift and a corresponding changing rate, i.e., Doppler rate. Although the Doppler rate is very small, it helps us improve the estimation accuracy of Doppler shift. Besides, the Doppler rate also contains target’s position information. Thus, a two-phase localization algorithm is proposed by jointly using the Doppler shift and the Doppler rate measurements. Intuitively, the Doppler rate is only loosely related to the target’s position and should not significantly contribute to the localization accuracy, but the Cramér-Rao lower bound (CRLB) analysis says differently. This can be explained by showing that the Doppler rate complements the vanishing degree of freedom (DoF) in the position information for far-field localization, i.e., the information fusion. The theoretical results are verified through extensive simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".