Acoustic Doppler velocity logs or acoustic Doppler current profilers, herein referred to as Doppler sonar, are important types of oceanographic instruments, which have been extensively used over the past 30 years. The precision of Doppler sonar is an important technical specification, which is a statement of velocity random error that partially or entirely averages out over a long period of time. This article focuses on acoustic robust velocity processing algorithm based on variational Bayes adaptive Kalman (VBAK) filter, known as the real-time estimation of velocity statistical characteristics, which may be contaminated by some unknown and nonstationary noise. It aims to solve the problem that the precision of the Doppler sonar is prone to be affected by the marine environment. The theory, simulation, and experimental results are compared with the standard Kalman filter algorithm. It shows that the VBAK algorithm may obtain a reasonable estimate of velocity even in the case of steep slope over 15° and the precision is effectively improved.

中文翻译：

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基于变分贝叶斯自适应卡尔曼滤波器的声学鲁棒测速算法

声学多普勒速度测井仪或声学多普勒海流剖面仪，在此称为多普勒声纳，是过去 30 年来广泛使用的重要类型的海洋仪器。多普勒声纳的精度是一项重要的技术指标，它是对在很长一段时间内部分或全部平均化的速度随机误差的陈述。本文重点介绍基于变分贝叶斯自适应卡尔曼 (VBAK) 滤波器的声学鲁棒速度处理算法，称为速度统计特性的实时估计，可能会受到一些未知和非平稳噪声的污染。旨在解决多普勒声纳精度易受海洋环境影响的问题。理论、模拟、并将实验结果与标准卡尔曼滤波算法进行比较。表明VBAK算法即使在超过15°的陡坡情况下也能得到合理的速度估计，有效提高了精度。