Abstract
This study was motivated by the infrared search and tracking (IRST) project. The investigation seeks to develop a technique that could detect the presence of a moving target in a cloud cluttered environment. Particularly, the signals, noise and clutters are unknown to the system. Thus, the correlation technique for image processing was developed, demonstrating its ability to detect moving targets of one pixel in size such as missiles and planes. A real-time image processor using this correlation technique was implemented. A Panoramic Imaging System, a 512 by 480 image processor at 30 frames per second was demonstrated. The demonstrated imaging system was operating at 120 mops (million operations per second) using an assembly- line processor architecture. The successful investigation of the correlation technique for image processing led to the developments of a correlation filter and the inspiration to develop the generalized filter. From the investigation, the author found that the Kalman filter, the Weiner filter and the correlation filter are special cases of a generalized filter. These filters can be related through a cost function in the constrained gain matrix of a generalized filter. However, in developing the correlation filter and the real-time imager, the correlation filter was observed to be a very effective noise and clutter rejecter and yet a very powerful detector. The filter was successfully applied to detection of pixel sized targets in noisy and cluttered IR images. Also it has been successfully applied to detection of intruders in cluttered, trees and bushes, video and IR images in security systems. This paper presents the derivation of the correlation filter for detection and estimation of unknown signals in unknown noise. Several noise rejection and cluttered rejection examples are presented.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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