对于自主水下车辆而言,运动感知声纳使用速度调节的U-Net转换器双分支条件生成对抗网络来消除自我噪音
Yufei Wang1,2, Yu Tian1, Shilong Li3
1State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
The Journal of the Acoustical Society of America
|January 13, 2026
概括
本研究介绍了Speed-UT2-CGAN,这是一个用于自动水下车辆 (AUV) 的新型声纳检测框架. 它有效地减少了取决于速度的噪音,增强了被动声纳监控能力.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 人工智能的人工智能是人工智能.
背景情况:
- 自主水下车辆 (AUV) 的被动声纳监测被非静止的,取决于速度的自我噪声显著降低.
- 现有的无声化方法很难适应AUV速度影响的动态噪声特性.
研究的目的:
- 为自动驾驶汽车开发一种运动感知声纳无噪声框架,有效地解决速度依赖的噪声.
- 提高被动声纳监控系统在具有挑战性的水下环境中的性能.
主要方法:
- 拟议的Speed-UT2-CGAN是一个双分支条件生成对抗网络,集成U-Net和变压器架构.
- 集成的AUV速度作为动态噪声适应的调节输入.
- 采用对抗性,时间域和频域损失函数的组合,用于全面的信号重建.
主要成果:
- 速度-UT2-CGAN显著优于传统方法和其他深度学习方法.
- 在-5dB输入时达到6.6dB的平均信号噪声比,并达到0.87.7的相关系数.
- 在浅水湖试验中,在各种AUV速度 (0,2和3节点) 中证明了有效性.
结论:
- 运动感知速度调节对于单传感器AUV系统中的被动声纳增强非常有效.
- 拟议的框架为改善AUV操作中的声纳数据质量提供了一个强大的解决方案.
- 结果验证了框架在受控条件下对现实世界的应用的潜力.
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