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- W2085808514 abstract "A new approach is described for detecting small targets in natural background clutter using multipletime and/or spectral-band imagery. The target detection problem is formulated as a composite hypothesistest in which the alternative hypothesis set is parameterized by the target amplitude and location within theimage set. Thus, the size and shape of the target are assumed to be known, but nothing is assumed for theamplitude or trajectory of the target. The probability density function for the image set is taken to be amultivariate normal density whose covariance is a function of the clutter spatial, temporal, and/or spectraldegrees of freedom. The assumption of small targets allows the clutter statistics to be estimated directlyfrom the input imagery. The resulting processor is a generalized likelihood ratio test in which theunknown target amplitude and location are replaced by their maximum likelihood estimates. Theassumption of spatially stationary background clutter allows the test statistic to be expressed as a set ofspatially filtered images in which each spatial frequency component is processed independently in timeand/or spectral band by a linear transfonnation operating on the input image set. The algorithm can beimplemented either as a block or recursive processor. The latter form requires less storage and allows theclutter statistics to be adaptively updated frame by frame. The approach has been successfully applied to avariety of synthetic and real imagery, including HI-CAMP I and II data. Performance estimates andsamples of unclassified processed imagery are presented." @default.
- W2085808514 created "2016-06-24" @default.
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- W2085808514 date "1990-10-01" @default.
- W2085808514 modified "2023-09-23" @default.
- W2085808514 title "Generalized likelihood ratio detector for small targets in stationary background clutter" @default.
- W2085808514 doi "https://doi.org/10.1117/12.21577" @default.
- W2085808514 hasPublicationYear "1990" @default.
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