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 contrast and sharpness to better differentiate targets from background and increase target detail. The software will be used to batch process large amounts of raw EOI images and save them in a format so that the user can scroll through the images using a standard image viewer. In 2008, the Bluefinl2 BMI system participated in multiple sea tests. The data collected from these missions proved that sensor fusion aboard an UUV was possible. Post Mission Analysis (PMA) also concluded that data fusion was successful. Both the RTG and the EOI participated in sea tests of the Bluefinl2 BMI System to evaluate, optimize and demonstrate a BMI capability. Specifically in 2008, this system was demonstrated at Panama City, FL and at AUVfest 2008 in Newport, RI. This paper focuses on the 2008 sea testing using the modified RTG and the EOI sensors and the ability to use near real-time detection.
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