基于深度学习的角度距离分解,用于主动声纳检测
Jichao Zhang1,2, Xiao-Lei Zhang1,2, Kunde Yang3
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
The Journal of the Acoustical Society of America
|December 17, 2025
概括
这项研究引入了一种深度学习方法,用于使用主动声纳检测水下目标. 该方法通过单独估计目标距离和角度来提高噪音环境中的准确性.
科学领域:
- 海洋科学和工程是海洋科学和工程.
- 水下声学 水下声学
- 信号处理 信号处理
背景情况:
- 传统的主动声纳目标检测方法在复杂的水下环境中与噪音,反响和干扰作斗争.
- 有效的水下声学数据有限,这给训练深度学习模型带来了挑战.
- 准确的水下目标检测对于海洋科学和工程应用至关重要.
研究的目的:
- 开发一种基于深度学习的强大方法,用于主动声纳目标检测.
- 解决在具有挑战性的水下条件下传统信号处理的局限性.
- 提高水下目标定位的准确性和可靠性.
主要方法:
- 建议采用深度学习方法,将目标检测分解为角度和距离估计.
- 深度学习模型用于预测目标距离和角度.
- 转移学习和模拟技术被用来克服有限的水下声学数据.
主要成果:
- 拟议的方法有效地检测复杂的声学环境中的水下目标.
- 实验结果表明,在具有挑战性的条件下,包括噪音和干扰,性能强.
- 整合估计的距离和角度提供了准确的最终目标定位.
结论:
- 深度学习为增强主动声纳目标检测能力提供了一个有希望的解决方案.
- 拟议的方法为传统的信号处理技术提供了更有效和更强大的替代方案.
- 转移学习和模拟是解决水下声学研究数据稀缺问题的可行策略.
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