Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
Bibliographic record
Abstract
Combining underwater communication and detection can reduce system size and power consumption as well as improve secrecy. This paper presents a waveform that integrates continuous phase modulation (CPM) for data communication with linear frequency modulation (LFM) for detection. It is shown that the velocity ambiguity and range performance with this waveform are similar to those with only LFM. An integrated underwater acoustic system is also proposed to achieve simultaneous communication and detection. A symbol suffix (SS) is introduced into the signal frame at the transmitter and a two-step algorithm is employed at the receiver for frequency and phase offset estimation. Results are presented, showing that this decreases the sensitivity to CPM and reduces the range ambiguity side lobes. Further, the proposed system can effectively recover the data from the received signal. The bit error rate (BER) is shown to be better than that of traditional systems without a symbol suffix. With changes in the signal-to-noise ratio (SNR), the bit error rate (BER) in underwater acoustic environments is comparable to that in an additive white Gaussian noise (AWGN) channel, indicating a significant improvement in communication performance within the underwater acoustic channel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".