Autonomous seafloor geotechnical property sensor Navy SBIR FY2006.2 Sol No.: Navy SBIR FY2006.2 Topic No.: N06-160 Topic Title: Autonomous seafloor geotechnical property sensor Proposal No.: N062-160-0763 Firm: Ocean Acoustical Services and Instrumentation Syst 5 Militia Drive Lexington, Massachusetts 02421 Contact: Kevin Heaney Phone: (781) 862-8339 Web Site: None Abstract: Remote classification of seafloor geotechnical properties is addressed in this SBIR. In shallow coastal waters, the current standard Navy databases do not contain information of suitable quality to determine pre-mission whether seafloor mines will be proud or buried, whether deployed bottom arrays will sink in mud or roll away from prescribed locations, or the performance of mine countermeasure (MCM) or anti-submarine warfare (ASW) systems. Sediment characteristics can be determined acoustically by using a combination of seismic reflection, reflection coefficient vs. grazing angle and interface wave travel time measurements. OASIS Inc. proposes to develop a system based upon existing Autonomous Undersea Vehicles (AUV) and novel acoustic measurement approaches for sediment characterization. By examining a combination of these hardware systems, algorithms and signal processing approaches, a system will be designed that has the cost, deployability, accuracy and resolution required for this challenging problem. OASIS proposes to work with the Woods Hole Oceanographic Institution to leverage existing AUV systems for remote, autonomous acoustic classification of seafloor sediments. Benefits: The development of autonomous, remote, clandestine vehicles for the purpose of seafloor sediment characterization has many applications within the naval community. In addition to this, this technology has commercial applications in the area of autonomous buried cable or wreckage localization. Autonomous sediment characterization can be used to remotely sample sedimentation from river run off or factories for environmental policy monitoring. The proposed system will also be applicable for adaptive sampling of the environment for sonar system performance optimization. Return