开放集船舶重识别：基于水下船辐射噪声的原始波形选择性核声学神经网络（SKANN）与跨航段评估协议
Open-Set Vessel Re-Identification from Underwater Ship-Radiated Noise with a Raw-Waveform Selective-Kernel Acoustic Neural Network (SKANN) and a Cross-Passage Evaluation Protocol
- Oravont Systems LLP（奥拉冯特系统有限责任合伙公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出开放集跨航段船舶重识别协议与原始波形SKANN模型，在40船体IARA数据上实现rank-1 0.35，支持分析分诊而非识别。
中文摘要 AI 辅助
水下声学目标识别已收敛于按船舶类型进行的闭集分类，该任务无法回答监测系统是否曾听到过该船体。我们在公开水听器数据上形式化了开放集、跨航段的船舶重识别问题，并指定了一种协议，该协议消除了获得高分的两条最便捷途径：基于MMSI/IMO的船体不相交划分、每个船体的航段不相交的图库与查询、源纯净图库，以及音频裁决的过境去重门控。我们描述了SKANN，一种原始波形编码器，其前端是由选择性核注意力融合的四尺度学习滤波器组，采用角度边际目标函数和增强机制训练，该机制扰动记录链路、环境噪声和多径，同时保留携带身份的窄带线。在40船体IARA图库（96个查询，98个航段候选）上，跨航段rank-1为嵌入的0.25和自动窄带音调比较器的0.26；两者在排名顶部统计上无显著差异，嵌入对列表其余部分的排序更可靠（AUC 0.82对比0.76），它们的分数融合达到rank-1 0.35——唯一达到名义显著性的对比，作为部分互补性的证据，而非推荐。仅过境去重就消除了16-21个百分点的表观rank-1优势，大于任何方法间差异。另外两个发现限定了公开数据能支持的范围：ShipsEar在身份协议下无法将船体身份与记录通道分离，跨网络微调对微调期间见过的船舶有帮助，但对未见船舶是无效结果。结果支持分析人员对排名短名单进行分诊，而非识别。检查点、验证嵌入、过境图和逐查询输出以CC-BY-4.0发布（doi: https://doi.org/10.5281/zenodo.22160138）。
英文摘要
Underwater acoustic target recognition has converged on closed-set classification by vessel type, a task that does not answer whether a monitoring system has heard this hull before. We formalise open-set, cross-passage vessel re-identification on public hydrophone data and specify a protocol that removes the two easiest routes to a high score: hull-disjoint splits keyed to MMSI/IMO, galleries and queries from disjoint passages of each hull, source-pure galleries, and an audio-adjudicated transit-deduplication gate. We describe SKANN, a raw-waveform encoder whose front end is a four-scale bank of learned filters fused by selective-kernel attention, trained with an angular-margin objective and an augmentation regime that perturbs recording chain, ambient noise and multipath while preserving the narrowband lines that carry identity. On a 40-hull IARA gallery (96 queries, 98 passage candidates), cross-passage rank-1 is 0.25 for the embedding and 0.26 for an automated narrowband-tonal comparator; the two are statistically indistinguishable at the top of the ranking, the embedding orders the rest of the list more reliably (AUC 0.82 vs 0.76), and their score fusion reaches rank-1 0.35 -- the only contrast that attains nominal significance, presented as evidence of partial complementarity, not as a recommendation. Transit deduplication alone removes a 16-21 point apparent rank-1 advantage, larger than any between-method difference. Two further findings delimit what public data can support: ShipsEar cannot separate hull identity from recording channel under an identity protocol, and cross-network fine-tuning helps vessels seen during fine-tuning but is a null result on unseen ones. The results support analyst triage over a ranked shortlist, not identification. Checkpoint, validation embeddings, transit map and per-query outputs are released under CC-BY-4.0 (doi:10.5281/zenodo.22160138).
