An Accurate Correlation-Type Doppler Estimator in Dynamic Underwater Acoustic Channels using Comb-Type Shift-Orthogonal Pilot Signals
Bibliographic record
Abstract
This paper presents a practical and accurate Doppler estimator for signals propagating in underwater acoustic (UWA) channels. Estimation is performed with relatively short shift-orthogonal pilot sequences due to their correlation properties. Pilot bursts are OFDM (orthogonal frequency division multiplexing). The receiver performs Doppler estimation by cross-correlating the received signal with the known pilot signal, resulting in a Doppler phase estimate from which the Mach number is determined in real-time. The estimator operates at the sample rate of the signal, which yields a Mach number estimate every sample. Therefore, instantaneous changes in the Mach number are known, thereby allowing the tracking of Doppler effects in dynamic channels. A design criterion for the estimator’s validity is derived, which relates bandwidth, carrier frequency and sequence length with Mach number. A MATLAB Simulink model of the estimator is presented. The results show its performance in synthetic channels that simulate relative motion between transmitter and receiver. The Cramer-Rao lower bound (CRLB) analysis shows the estimator achieving a variance below 10 -4 for SNRs above 20 dB. This is a near-optimal estimator. Additionally, compared to other estimators, the computational complexity is considerably lower and the range of valid Mach numbers that it can address is higher.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".