Accession Number : ADA180925
Title : Detecting a Target of Unknown Brightness in Clutter.
Descriptive Note : Interim rept. Jan-Sep 86,
Corporate Author : NAVAL RESEARCH LAB WASHINGTON DC
Personal Author(s) : Osgood,Charles F. ; Priest,Richard G.
Report Date : 27 APR 1987
Pagination or Media Count : 12
Abstract : Standard infrared target detection algorithms based on the Bayes decision rule or Neyman-Pearson test are optimal only when testing for targets of known strength (brightness). The discriminant functions used in these tests depend, in functional form, on the assumed brightness of the targets being sought. That is to say, they are not uniform with respect to target brightness. Linear uniform tests such as the matched filter are not near optimal for multidimensional cases. The case of interest here is a multidimensional one-the detection of moving targets in differenced mosaic images. The uniform tests that we consider is the generalized maximum likelihood (GML) tests. Three implementations are discussed. Results are presented that indicate that the uniform GML test compares favorably with the optimal Bayes decision rule for detection of moving targets in mosaic imagery.
Descriptors : *MOVING TARGETS, *THERMAL TARGETS, *INFRARED DETECTION, INFRARED IMAGES, DISCRIMINATE ANALYSIS, MATCHED FILTERS, MAXIMUM LIKELIHOOD ESTIMATION, MOSAICS(DETECTORS), OPTIMIZATION, ALGORITHMS, BAYES THEOREM, TARGETS, BRIGHTNESS, DECISION MAKING
Subject Categories : Infrared Detection and Detectors
Distribution Statement : APPROVED FOR PUBLIC RELEASE