LAUSR

As an acoustic communications medium, water is characterized by frequency dependent attenuation, short range, very low bandwidth, scattering, and multi-path. It is generally difficult to acoustically communicate even terse messages underwater much less images. For the naval mine counter-measures mission, there is value in transmitting images of mine-like objects, acquired by side-scan sonar on-board unmanned underwater vehicles, to the above-water operator for review. The contribution of this paper is a methodology and implementation, based on vector quantization, to compress and transmit snippets of side-scan sonar images from underway unmanned underwater vehicles to an operator. The work has been validated through controlled indoor tank tests and several at-sea trials. The fidelity of the received images is such that trained operators can recognize targets in the received images as well as they would have in the original images. Future work investigates machine learning to improve the compression basis and psycho-visual studies for the specialized skill of feature recognition in sonar images.