The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jun. 28, 2005
Filed:
May. 14, 2004
Christopher W. Mutz, Cambridge, MA (US);
Leonid I. Perlovsky, Brookline, MA (US);
Robert J. Linnehan, Brighton, MA (US);
Christopher W. Mutz, Cambridge, MA (US);
Leonid I. Perlovsky, Brookline, MA (US);
Robert J. Linnehan, Brighton, MA (US);
The United States of America as represented by the Secretary of the Air Force, Washington, DC (US);
Abstract
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.