DMD-Based Background Flow Sensing for AUVs in Flow Pattern Changing Environments

Abstract

This letter is concerned with real-time background flow field estimation using distributed pressure sensor measurements for autonomous underwater vehicles (AUVs). The goal of this study is to enhance environmental perception of AUVs especially in dynamic environments with changing flow patterns. Dynamic mode decomposition (DMD), a data-driven model reduction approach, is adopted to model the dynamic flow field using spatial basis modes and their corresponding temporal coefficients. This letter computes the DMD modes offline and applies a Bayesian filter to assimilate distributed pressure sensor measurements to estimate the DMD coefficients in real time. Further, fast Fourier transform (FFT) analysis is used to determine the flow pattern/model represented by DMD modes. Aiming to address the flow sensing problem in flow pattern changing environments, the proposed approach is expected to greatly improve environmental perception of AUVs in dynamic and complex flows. Both simulation and experimental results of flow sensing using three testing prototypes are presented to validate the proposed method.