United States of America As Represented by the Secretary of the Air Force

University of Memphis Research Foundation

Leonid Perlovsky

Robert Kozma

Christopher W Mutz

Robert J Linnehan

Methods, computer-readable media, and systems are provided for the detection of propagating phase gradients using model field theory of non-Gaussian mixtures. One embodiment of the invention is directed to a method for identifying phase cones in a data set. The method includes generating an initial set of values of parameters (&#x3be;,&#x3b7;,t) for a preset number of cones, initializing a covariance matrix with high values, evaluating the ratio of volume of a phase cone over total observed space-time volume, comparing the data set with a model, estimating a class probability density function, and generating a revised set of values of parameters (&#x3be;,&#x3b7;,t).

Methods of detection of propogating phase gradients using model field theory of non-gaussian mixtures

The present invention includes an application of a dynamic logic algorithm to detect slow moving targets. Show moving targets are going to be moving in the range from 0-5 mph. This could encompass troop movements and vehicles or convoys under rough terrain. The method can be defined as a seven step process of detecting slow moving targets using a synthetic aperture radar (SAR), said slow moving targets being objects of interest that are moving in the range from 0-5 mph, wherein this method is composed of the steps of receiving SAR signal history data having an SAR image; assuming a presence of slow moving target in a SAR image based-on range, cross-range position, and velocity; assuming a presence of clutter; assigning target and clutter models that are probability distribution functions (pdf) that are defined to account for every pixel in the SAR image, wherein the target is modeled using a sum of Gaussians fitted along the target shape model, while the clutter is modeled with a uniform distribution; computing a 'target present' predetermined threshold value; converging the target model to a minimum variance value; and comparing the target model minimum variance value to the predetermined threshold to determine if a target is present or absent.

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Brookline, MA, United States of America

Leonid Perlovsky