Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

Abstract

Autonomous underwater vehicles require accurate navigation. Techniques such as image registration using consecutive acoustic images from a sonar have shown promising results for this task. The implementation of such techniques using sonar images augmented with deep learning (DL) networks demonstrate high navigation accuracy; this is possible even with highly compressed images. The sonar images are estimates of sampled in time (with a ping period) magnitudes of channel impulse responses representing the underwater acoustic environment. More information about the environment is contained in (almost) continuous in time estimates of the channel impulse responses. Such estimates can be obtained using full-duplex technology. Rather than using sonar images, this paper investigates the use of channel impulse response estimates for underwater platform motion estimation. The proposed system uses a single projector and a small number of receiving transducers installed on the moving platform. A DL network is used to estimate the motion in two degrees of freedom (forward/backward and sideways), using two or more consecutive impulse response estimates as the input. To train the DL network, a specially designed simulator is used to model the underwater acoustic environment, populated with multiple objects spread on the seafloor. The proposed technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher accuracy of motion estimation, compared with techniques based on the processing of sonar images, e.g., the error achieved with the technique proposed in this paper is 1.7% of the maximum platform displacement, compared to 4% achieved with a technique using sonar images. The navigation accuracy is further illustrated by examples of estimation of complex trajectories.

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

“…With the vast majority of the world's oceans still unexplored, the ability to accurately detect and locate underwater targets such as minerals, oil and gas deposits, and marine life is essential for sustainable and effective resource management. Underwater target detection technology includes a variety of methods, such as sonar and acoustic imaging [1][2][3], magnetic and electromagnetic sensing [4,5], and visual inspection using remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). These technologies allow researchers and industry professionals to map the seafloor, identify potential resource deposits, and locate marine life for conservation or fishing purposes.…”