The observation of closely-spaced objects using limited-resolution
Infrared (IR) sensor systems can result in merged object measurements on the focal plane. These Unresolved Closely-Spaced Objects (UCSOs) can significantly hamper the performance of surveillance systems. Algorithms are desired which robustly resolve UCSO signals such that (1) the number of targets, (2) the target locations on the focal plane, (3) the uncertainty in the location estimates, and (4) the target intensity signals are correctly preserved in the resolution process. This paper presents a framework for obtaining UCSO resolution while meeting tracker real-time computing requirements by applying processing algorithms in a hierarchical fashion. Image restoration techniques, which are often quite cheap, will be applied first to help reduce noise and improve resolution of UCSO objects on the focal plane. The CLEAN algorithm, developed to restore images of point targets, is used for illustration. Then, when processor constraints allow, more intensive algorithms are applied to further resolve USCO objects. A novel pixel-cluster decomposition algorithm that uses a particle distribution representative of the pixel-cluster intensities to feed the Expectation Maximization (EM) is used in this work. We will present simulation studies that illustrate the capability of this framework to improve correct object count on the focal plane while meeting the four goals listed above. In the presence of processing time constraints, the hierarchical framework provides an interruptible mechanism which can satisfy real-time run-time constraints while improving tracking performance.