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Acoustic Robust Velocity Measurement Algorithm Based on Variational Bayes Adaptive Kalman Filter

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.