Hunting sea mines with UUV-based magnetic and electro-optic sensors

The US Navy (USN) has recognized the need for effective buried-mine hunting as one of its Organic Mine Countermeasures (MCM) Future Naval Capabilities. Current thinking envisions a two-step process for identifying buried mines. First, an initial survey, or Search-Classify-Map (SCM) mission, will be performed using low-frequency synthetic aperture sonar (SAS). Second, a Reacquire-and-Identify (RI) mission will provide confirmatory final classification by reacquiring the target, at close range, with magnetic, acoustic, and electro-optic sensors, and evaluating properties such as geometric details and magnetic moment that can be fused to identify or definitively classify the object. The goal is to demonstrate a robust capability to identify buried sea mines through sensor fusion. Specifically, the classification results of a passive magnetic sensor and an electro-optic sensor will be generated for fusion with the results from a short-range bottom-looking sonar, with all three sensors co-residing and operating simultaneously on an Unmanned Underwater Vehicle (UUV). The Bluefinl2 Buried Mine Identification (BMI) System will be used as the platform to develop a capability for the identification of buried mines. This system houses the bottom looking sonar, the Real-time Tracking Gradiometer (RTG), and an Electro-Optic Imager (EOT). This paper will address the applications of the RTG, EOI, and data fusion results with bottom looking sonar. The objective for the RTG is the enhancement of the processing that extracts target locations and magnetic moments from the raw RTG data. In particular, we are adding a capability to conduct real-time processing capability to provide autonomous target classification and localization results soon after the UUV passes the target, while the system is still performing the mission. These results will be shared with the vehicle or other sensors for transmission back to a base station when the vehicle surfaces. The objectives for the EOI include additions to the control software and the development of a set of versatile image processing techniques. A significant goal is to develop the ability to make images viewable remotely over the vehicle's RF link. This allows for a quick review of contacts and improved flexibility in mission planning and execution. Image processing goals included the development of image enhancement algorithms that could be applied to all EOI data. The intent of the enhancement algorithms is to enhance image contr