在浅水波导中通过活跃的深层复杂残余网络定位水下源
Lei Yang1,2, Zhixiang Wu1, Longyu Jiang1
1Laboratory of Marine Information Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
The Journal of the Acoustical Society of America
|October 16, 2025
概括
本研究引入了用于水下来源定位的积极学习框架,将准确度提高了12%,并减少了偏差. 它大大减少了对深度学习模型的数据标签需求.
科学领域:
- 声学 声学 在声学上
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 目前用于水下源定位的深度学习通常使用实值数据,可能利用不足的信息.
- 高标记的数据要求和适应能力差限制了深度学习应用在各种水下环境.
研究的目的:
- 开发一个高效和可适应的深度学习框架,用于水下来源的定位.
- 解决现有方法中实值表示和广泛标记的局限性.
主要方法:
- 提出了一个端到端的积极学习框架 (ADCRN) 与一个深度复杂的残余网络 (DCRN-CCL) 骨干.
- 整合了适应加权的不确定性-多样性查询策略和基于模型的转移学习.
- 采用复杂域循环损失和基于平均值的概率融合来增强特征歧视.
主要成果:
- 与实值深度学习方法相比,实现了大约12%的更高准确度,并将平均绝对偏差降低了1.2%.
- 在SWellEx-96数据集上保持了与DCRN-CCL可比的性能.
- 仅需要37.89%的总样本进行标记和69.87%的培训时间.
结论:
- 拟议的ADCRN框架有效地提高了水下来源定位的准确性和效率.
- 复杂值特征表示和主动学习显著降低了数据依赖性,提高了概括性.
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