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.