Motion-aware self-localization for underwater networks

A myriad of ocean processes affect life on the planet and are a source of intrigue to oceanographers and scientists. Understanding these processes and their interactions with currents requires collection of relevant data. A network of mobile platforms can be used to learn the correlation of processes in space and over time. To do this, data samples collected by nodes have to be annotated with location information. Given limited access to Global Positioning Systems underwater, collaborative self-localization schemes applied periodically are well-suited for this purpose. However, the specific nature of the underwater acoustic environment introduces significant error during network self-localization due to the combined effect of large latencies in communication and node mobility. We propose a method to account for these effects thus significantly improving the accuracy of position estimates.