一个带分和子带权重的水下声学目标识别系统
Yuxuan Wang1,2, Jiawei Ren1,2, Yuan Xie1,2
1University of Chinese Academy of Sciences, Beijing 100190, China.
The Journal of the Acoustical Society of America
|August 27, 2025
概括
一个新的分频权重网络 (BSWNet) 通过保留频段信息来改善水下声学目标识别 (UATR). 这种方法在具有挑战性的声环境中提高了准确性和抗噪能力.
科学领域:
- 信号处理
- 机器学习
- 听力学
背景情况:
- 水下声学目标识别 (UATR) 方法往往会导致能量分布模糊,频段位置信息丢失.
- 现有的数据驱动机器学习方法在准确建模子频段特征关系方面存在局限性.
研究的目的:
- 推出一个新的带分割权重网络 (BSWNet) 增强UATR.
- 解决UATR系统中能量分布模糊和位置信息丢失的局限性.
- 提高水下声学目标识别的准确性和稳定性.
主要方法:
- 拟议的BSWNet使用频段分割来将声谱划分为子频段.
- 包含带位置编码 (BPE) 来保存原始子带的位置信息.
- 使用频段重量注意 (BWA) 来提取不同子频段的重要性重量.
主要成果:
- 在ShipsEar和DeepShip数据集上,BSWNet表现出与基线系统相比的显著性能改善.
- 在-10dB的信号与噪声比率 (SNR) 中获得了超过10%的精度,这表明噪声耐受性得到了增强.
- 废弃研究证实了BPE和BWA模块在提高网络性能方面的有效性.
结论:
- BSWNet有效地减轻了能量模糊,并保留了UATR中的位置信息.
- 与现有的UATR方法相比,拟议的模型提供了更高的准确性和抗噪能力.
- 对于水下声学目标识别应用来说,BSWNet是一个有前途的进步.
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