High resolution, high data rate sensor streams acquired from the Navy Shared Reconnaissance Pod (SHARP), encompassing unsurpassed resolution EO and IR sensors, covering large tactical areas with detailed surveillance information, will overwhelm current signal processing and communications capabilities. However, the value and utility of these data streams is dependent on their subsequent
exploitation and timely dissemination to appropriate commanders. This situation renders real-time surveillance infeasible without significant advances in each of these areas: signal processing, communications, and interpretation. Data compression, encryption, and other related technologies play a vital role here. Here we focus on the target recognition problem from an ultra-high resolution SHARP sensor suite, specifically on the detection in the EO domain. The theory of correlation filters (MACH, MACE, etc.), developed by Casasent and company at CMU has been typically used for classification purposes in the past. Herein we develop innovative low-complexity Correlation Eigen-Filters (CEFs), which have the unique advantage of offering detection capability for one or multiple objects, over a wide range of aspect angles (up to full 360 degrees), using as few as a single filter. In the paper, we develop a theoretical analysis of the CEF filter design, and provide some application examples. Figure 1 illustrates a case in point: various military aircraft are detected with perfect performance (Pd = 1.0, Pfa = 0) by training CEF filters on examples aircraft in other imagery, and testing on sequestered data. We not only diverge from traditional correlation-filter methods in that we use the correlation filter as a detector, but also to develop a novel feature space in which to do discrimination analysis, figure 1c.