NeTS: Cognitive Networking of the Oceans: Localization and Tracking Fundamentals

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Undersea wireless communications and networking has a wide range of potential scientific, commercial, and military applications, but is still a daunting task due to the very nature of the water propagation medium. Fundamental to the success of undersea communications is robust localization and tracking to support position-aware data routing and autonomous navigation in the GPS-less oceanic environment. Current state-of-the-art approaches for undersea localization and tracking consider expensive, power-intense inertial measurement sensors, geophysical-based imaging, and acoustic signaling techniques. However, long propagation delays, Doppler effects due to water movement, and the highly dynamic multipath nature of the undersea environment result in significant errors and outliers in acoustic and imaging measurements. This project aims to significantly advance (five-fold in precision) the state of the art in 3D-passive undersea acoustic localization by providing novel algorithmic solutions to the problem of estimating the angle of arrival of undersea propagating signals in the presence of potentially faulty measurements. The algorithms will then be embedded in in-house-developed programmable underwater acoustic modems to form an experimental four-node network for evaluation and demonstration. Localization capabilities will be demonstrated in communication, command and control application scenarios for remote wireless navigation and surveillance. The project will integrate research and education at all University-degree levels by establishing cross-listed graduate/undergraduate courses on undersea acoustic localization and its applications and activities to reach out to groups of middle and high-school students. Technology transfer activities in this field will be also pursued.The project pursues a paradigm shift in how signal Direction-of-Arrival (DoA) estimation is carried out over undersea acoustic links by deviating from the familiar L2-norm-subspace decomposition theory and its variants and considering, for the first time, L1-norm signal subspace decomposition. Motivated by the resistance of L1-norm-derived subspaces against outliers and/or data corruption and the recent computational advances in the field, this project will employ novel L1-norm maximum-projection principal-component analysis of the antenna array measurements to design a novel, outlier- Doppler- and multipath-resistant DoA estimation method for undersea acoustic networks. L1-norm localization and tracking will support position-aware data routing and autonomous navigation applications in the GPS-less oceans under occasional severe dynamic signal scattering and path loss.