Extreme Computing with Light, Low Power, and Small Equipment (ECLIPSE)

The military’s recognition of the need to share battlefield intelligence between all branches of the armed services has led to the development of the Joint Fires Network (JFN). In JFN, intelligence data from the many sensors deployed must be assimilated for targeting information. Computers perform some of these operations while people perform others. The bottom line is that there are massive amounts of data that are parsed and reviewed in looking for potential threats. Conventional systems have provided this capability at non-real-time rates, but have power, space, and manpower requirements that can only be fulfilled by large deck carriers. Advanced Optical Systems proposes to develop a Hybrid Image Processor to assist with this crushing data stream. The Hybrid Image Processor has the ability to sort through video rate input images (RS170, IR, Radar, etc.). It detects, locates, recognizes, cues, and even provides identification and aimpoint selection functions to an operator. The sampled 2D Fourier Transform of the input image by post-processing algorithms such as Ring/Wedge, Neural Networks, and Discrimination are exploited; all with a predicted envelope of <10 lbs, <10 watts, and <200 in3. This novel approach, coupled with matched filter correlation, provides a powerful target recognition system.