在多频段合成光圈声纳与合变化自动编码器的平台运动估计
Angeliki Xenaki1, Yan Pailhas1, Alessandro Monti1
1Centre for Maritime Research and Experimentation, STO-NATO, 19126 La Spezia, ItalyAngeliki.Xenaki@cmre.nato.int, Yan.Pailhas@cmre.nato.int, Alessandro.Monti@cmre.nato.int.
JASA express letters
|February 20, 2024
概括
高分辨率的合成孔径声纳成像需要精确的平台运动估计. 本研究介绍了一种机器学习方法,使用层次变异推理来提高多频段SAS系统中无监督微导航准确度.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器学习是机器学习.
背景情况:
- 在合成孔径声纳 (SAS) 中的一致处理需要高度准确的平台运动估计和高分辨率成像补偿.
- 当外部定位数据不可靠或不可用时,通过传感器估计的微导航方法至关重要.
研究的目的:
- 开发一种先进的无监督机器学习方法,用于多频段SAS系统中的微导航.
- 通过利用多输入多输出 (MIMO) 配置来提高平台运动估计的准确性.
主要方法:
- 利用基于变量贝叶斯推理的机器学习方法,用于无监督,数据驱动的微导航.
- 采用分层变异推理方案与多频段SAS系统的MIMO安排相结合.
- 在推理方案中实施了一种自我监督机制,以学习平台运动.
主要成果:
- 通过提出的等级变量推理方法,通过提议的等级变量推理方法,实现了更好的微导航准确性.
- 证明了利用MIMO配置用于增强运动估计的有效性.
- 验证了学习过程的无监督,数据驱动的性质.
结论:
- 开发的层次变异推理方法显著提高了多频段SAS中的微导航精度.
- 在自主监督学习框架中利用MIMO配置为精确的平台运动估计提供了强大的解决方案.
- 这种方法在具有有限或不准确外部导航数据的环境中对高分辨率的SAS成像尤为有价值.
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