Detecting a Target of Unknown Brightness in Clutter.
Interim rept. Jan-Sep 86,
NAVAL RESEARCH LAB WASHINGTON DC
Pagination or Media Count:
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.
- Infrared Detection and Detectors