Adaptive multi-sensor biomimetics for unsupervised submarine hunt (AMBUSH): Early results

Stéphane Blouin

doi:10.1117/12.2078710

Abstract

Underwater surveillance is inherently difficult because acoustic wave propagation and transmission are limited and unpredictable when targets and sensors move around in the communication-opaque undersea environment. Today's Navy underwater sensors enable the collection of a massive amount of data, often analyzed offtine. The Navy of tomorrow will dominate by making sense of that data in real-time. DRDC's AMBUSH project proposes a new undersea-surveillance network paradigm that will enable such a real-time operation. Nature abounds with examples of collaborative tasks taking place despite limited communication and computational capabilities. This publication describes a year's worth of research efforts finding inspiration in Nature's collaborative tasks such as wolves hunting in packs. This project proposes the utilization of a heterogeneous network combining both static and mobile network nodes. The military objective is to enable an unsupervised surveillance capability while maximizing target localization performance and endurance. The scientific objective is to develop the necessary technology to acoustically and passively localize a noise-source of interest in shallow waters. The project fulfills these objectives via distributed computing and adaptation to changing undersea conditions. Specific research interests discussed here relate to approaches for performing: (a) network self-discovery, (b) network connectivity self-assessment, (c) opportunistic network routing, (d) distributed data-aggregation, and (e) simulation of underwater acoustic propagation. We present early results then followed by a discussion about future work.

Citation Download Citation

Stéphane Blouin, Stéphane Blouin, "Adaptive multi-sensor biomimetics for unsupervised submarine hunt (AMBUSH): Early results", Proc. SPIE 9248, Unmanned/Unattended Sensors and Sensor Networks X, 92480O (17 October 2014); doi: 10.1117/12.2078710; https://doi.org/10.1117/12.2078710

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