Accession Number : ADA264353


Title :   Outlier Detection in Infrared Signatures


Descriptive Note : Final rept. 15 Mar 91-14 Sep 92,


Corporate Author : NICHOLS RESEARCH CORP NEWPORT BEACH CA


Personal Author(s) : Chernick, Michael ; Magnuson, Jon A


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a264353.pdf


Report Date : Jan 1992


Pagination or Media Count : 24


Abstract : For a number of years, simulated long wavelength infrared (LWIR) signatures have been used to determine the ability to classify military targets and decoys. Such signatures sometimes exhibit specular behavior, a characteristic displaying a sudden increase in radiant intensity of short duration. This specular behavior is sporadic and is as likely to show up for targets as it is for decoys. Unfortunately, if these outliers (i.e. the specular occurrences) are not removed from the data. the estimated performance of discrimination algorithms can be misleading. Statistical outlier detection provides an useful approach for finding and removing the outliers caused by specular occurrences. This paper considers the statistical properties of the outlier detection algorithms as applied to simulated LWIR signatures. We consider possible statistical models for outliers in order to determine whether or not modifications might minimize the number of outliers left in the signature after editing and minimize the number of good observations deleted from the signature. Ultimately, we are seeking the data editing algorithm which produces the best possible discrimination performance.


Descriptors :   *INFRARED DETECTION , *INFRARED SIGNATURES , ALGORITHMS , MODELS , MODIFICATION , OBSERVATION , TARGETS , DECOYS , BEHAVIOR , DISCRIMINATION , LONG WAVELENGTHS , NUMBERS , EDITING , RADIANT INTENSITY


Subject Categories : Infrared Detection and Detectors


Distribution Statement : APPROVED FOR PUBLIC RELEASE