A Novel FFT-Assisted Background Flow Sensing Framework for Autonomous Underwater Vehicles In Dynamic Environment with Changing Flow Patterns

Due to the harsh and unknown underwater environment, the question of how autonomous underwater vehicles (AUVs) should navigate and maneuver, especially in a dynamic environment with changing flow patterns, is still largely open. This paper presents a systematic background flow sensing framework, which plays an important role in improving the navigation/control intelligence of AUVs. This flow sensing framework utilizes distributed pressure measurements of AUVs to estimate surrounding flow fields. The proposed method first determines the flow pattern/model around AUVs based on fast Fourier transform (FFT) spectrum analysis and then uses recursive Bayesian estimation and dynamic mode decomposition (DMD)-based modeling to identify model parameters. This method is capable of sensing background flow fields even in flow pattern changing environments, e.g., open waters in real-world scenarios, thus dramatically expanding the application scope of the existing flow sensing methods. Simulation results are provided to demonstrate the effectiveness of the proposed flow sensing method.