Development of Analysis Techniques for Predicting Magnetic Anomaly Detection (MAD) Equipped UAV Performance in Naval Anti-Submarine Warfare Environment
Navy SBIR 2014.1 - Topic N141-010
NAVAIR - Ms. Donna Moore - [email protected]
Opens: Dec 20, 2013 - Closes: Jan 22, 2014
N141-010 TITLE: Development of Analysis Techniques for Predicting Magnetic Anomaly Detection (MAD) Equipped UAV Performance in Naval Anti-Submarine Warfare Environment
TECHNOLOGY AREAS: Air Platform, Sensors, Battlespace
ACQUISITION PROGRAM: PMA 264
RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted". The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.
OBJECTIVE: Develop a software simulation tool, or Tactical Decision Aid (TDA), for predicting the Probability of Detection (Pd) of a Magnetic Anomaly Detection (MAD) equipped Unmanned Aerial Vehicle (UAV) against current submarine threats factoring in the complexities of the MAD system performance, magnetic environmental noise, UAV performance, target parameters and Area of Uncertainty (AOU)
DESCRIPTION: MAD equipped UAVs are being developed to accomplish the detection, localization and track phases of the Anti-Submarine Warfare (ASW) mission. The MAD ASW mission currently relies on paper MAD Operational Effectiveness (MOE) charts which are prepared for selected areas throughout the world and display predicted environmental noise levels only. There is no other TDA available for the MAD mission which factors in the sensor performance, including environmental effects, or aircraft and target parameters to determine a Pd. Without such a tool, the ASW mission commander has no current analysis of the MAD UAV effectiveness and will have no basis to determine the success of finding the target, thus possibly wasting time and assets in the most critical part of the ASW mission.
An integrated tool is needed for predicting the effectiveness of the MAD UAV in detecting the target which factors in all the relevant parameters of the MAD system. The TDA should factor in UAV performance, background magnetic noise, target parameters and the initial AOU size.
The tool must include inputs for:
Once all these factors are accounted for, the tool should provide the ASW mission commander with Pd result so he can make the GO/NO-GO decision to launch the MAD UAV and also provide the optimal search tracks for the UAV to follow.
PHASE I: Design and develop a detailed software integration and implementation plan showing how the above parameters will be combined into a single simulation tool for predicting the probability of detection of a MAD UAV given certain initial parameters.
PHASE II: Develop and implement the tool and demonstrate its applicability to the ASW mission using modeled parameters.
PHASE III: Further develop and integrate the tool for appropriate aircraft and mission systems
PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: With slight modifications to remove target signatures, the tool can be used as a mission planning tool for geologic surveys. NOAA has interest in a MAD UAV for wreck detection and mapping.
2. Maus, S., S. Macmillan, S. McLean, B. Hamilton, A. Thomson, M. Nair, and C. Rollins, 2010, The US/UK World Magnetic Model for 2010-2015, NOAA Technical Report NESDIS/NGDC.
3. Avera, W., Nelson, J. & Chase, H., (2003). VP-08 Environmental MAD Survey of the Bay of Fundy. Journal of Underwater Acoustics, P967.
KEYWORDS: MAD, Magnetic Anomaly Detection, UAV, MOE Charts, Environmental modeling, ASW