N112-132 TITLE: Automated Networked Torpedo Defense

TECHNOLOGY AREAS: Information Systems, Electronics

ACQUISITION PROGRAM: Program Executive Office, Integrated Warfare Systems (PEO IWS) 5E

OBJECTIVE: Investigate and develop automated and autonomous networked torpedo detection, classification, and localization information fusion algorithms to support coordinated and networked Torpedo Defense using relevant data from ship self-defense systems with torpedo detection, classification and localization (TDCL) capability. These information fusion algorithms will receive relevant data from all networked platforms with TDCL capability. From this data, the information fusion algorithms will generate alerts concerning possible torpedoes, in order to reduce risk to friendly units and optimize counter-fire.

DESCRIPTION: Future naval platforms (e.g., DDG, CV, LCS) will have self-defense systems that can detect, classify, and localize torpedoes. Recent advances in torpedo defense have focused on generating a Torpedo Picture on each platform of interest using that platform's relatively small number of sensors. In an environment with multiple platforms, it should be possible to improve overall military utility by creating a networked Torpedo Picture. The aim of this project will be to evaluate candidate information fusion algorithms technologies that will perform automated and autonomous networked torpedo detection, classification, and localization. Operationally critical performance metrics to be improved by transitioning these algorithms into the fleet will include reduced risk from torpedoes and reduced operator workload.

PHASE I: Develop and assess feasibility of automated and autonomous networked torpedo detection, classification, and localization information fusion algorithms that utilize best-available information fusion techniques and are suitable for transition into Navy systems such as the Undersea Warfare Decision Support System (USW-DSS) or the Aircraft Carrier Tactical Support Center (CV-TSC). Emphasis will be placed on future military utility (i.e., producing effective tools that reduce fleet vulnerability to torpedoes).

PHASE II: Consistent with the Navy Advanced Processor Build (APB) process, fully develop prototype automated and autonomous networked torpedo detection, classification, and localization information fusion algorithms to evaluate their operation and ability to effectively provide automated and autonomous counter-fire recommendations and alerts.

PHASE III: Embed and demonstrate the Phase II automated and autonomous torpedo detection, classification, and localization algorithms within a prototype ASW command and control system such as USW-DSS or CV-TSC. Demonstrate and report on performance during at-sea trials.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This networked alerting technology could be applicable to a wide range of civilian applications of autonomy and automation including the use of unmanned systems for homeland defense, automation of building, facility, and port security, search and rescue, and other first responder applications.

REFERENCES:
1. www.dtic.mil/descriptivesum/Y2008/Navy/0603747N.pdf.

2. J.E. Baker, C.A. Butler, W. R. Monach, and T.R. McSherry, "Automated Torpedo Classification and Alerting Using Bayesian Methods (U)," JUA(USN) 58, 1171-1198 (2008).

3. Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare Library) by D. Hall and S. McMullen.

4. Handbook of Multisensor Data Fusion (Electrical Engineering & Applied Signal Processing) by David L. Hall (Editor), James Llinas (Editor).

5. Estimation with Applications to Tracking and Navigation: Algorithms and Software for Information Extraction (Wiley, 2001) by Y. Bar-Shalom, X. R. Li and T. Kirubarajan.

6. Probabilistic Multi-Hypothesis Tracker: Addressing Some Basic Issues (Proceedings of the IEE � Radar, Sonar and Navigation) by Peter Willett with M. Efe and Y. Ruan.

KEYWORDS: torpedo defense; information fusion; detection; classification; localization

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