利用耦合变异自动编码器进行多波段合成孔径声纳中的平台运动估计。

IF 1.2 Q3 ACOUSTICS
Angeliki Xenaki, Yan Pailhas, Alessandro Monti
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引用次数: 0

摘要

合成孔径声纳(SAS)的相干处理需要亚波长精度的平台运动估计和补偿,以实现高分辨率成像。在没有导航仪器提供定位信息或定位信息不够准确的情况下,微导航(即通过传感器进行平台运动估计)至关重要。有人提出了一种基于变异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。在此,利用多波段 SAS 系统的多输入多输出安排,并结合分层变异推理方案,对平台运动进行自我监督学习,从而提高微导航精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Platform motion estimation in multi-band synthetic aperture sonar with coupled variational autoencoders.

Coherent processing in synthetic aperture sonar (SAS) requires platform motion estimation and compensation with sub-wavelength accuracy for high-resolution imaging. Micronavigation, i.e., through-the-sensor platform motion estimation, is essential when positioning information from navigational instruments is absent or inadequately accurate. A machine learning method based on variational Bayesian inference has been proposed for unsupervised data-driven micronavigation. Herein, the multiple-input multiple-output arrangement of a multi-band SAS system is exploited and combined with a hierarchical variational inference scheme, which self-supervises the learning of platform motion and results in improved micronavigation accuracy.

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