Enhancing autonomous navigation: acoustic tomography through synthetic data modeling

Abstract

This paper tackles the new utilization of acoustic tomography and synthetic data modeling to improve autonomous navigation systems, especially in environments where traditional navigation tools, such as GPS, are either restricted or absent. Thus, acoustic tomography, based on sound waves, as well as synthetic data modeling which creates unrealized but realistic navigation scenarios, seems to be a very useful way of enhancing accessible navigation in terms of reliability, precision, and adaptability. This research via a comprehensive literature review, review, and synthesis of current research articulates the promise of these technologies to transform navigation systems. It focuses on the approach in the integration of acoustic sensing with computational models in developing the system autonomy, covering the technological advancements and the problems. Besides, implications of the research to the whole range of applications from underwater exploration to urban autonomous vehicles specify the requirement of innovation and exploration development in this sphere. By providing a broad review and evaluation of the interactions of acoustic tomography with synthetic data modeling, this paper seeks to promote progress in autonomous navigation technologies, laying the groundwork for the future studies and engineering work in navigation through the world's most difficult environments.