Oct 1, 1990
Journal name not available for this finding
A new approach is described for detecting small targets in natural background clutter using multiple time and/or spectral-band imagery. The target detection problem is formulated as a composite hypothesis test in which the alternative hypothesis set is parameterized by the target amplitude and location within the image set. Thus, the size and shape of the target are assumed to be known, but nothing is assumed for the amplitude or trajectory of the target. The probability density function for the image set is taken to be a multivariate normal density whose covariance is a function of the clutter spatial, temporal, and/or spectral degrees of freedom. The assumption of small targets allows the clutter statistics to be estimated directly from the input imagery. The resulting processor is a generalized likelihood ratio test in which the unknown target amplitude and location are replaced by their maximum likelihood estimates. The assumption of spatially stationary background clutter allows the test statistic to be expressed as a set of spatially filtered images in which each spatial frequency component is processed independently in time and/or spectral band by a linear transfonnation operating on the input image set. The algorithm can be implemented either as a block or recursive processor. The latter form requires less storage and allows the clutter statistics to be adaptively updated frame by frame. The approach has been successfully applied to a variety of synthetic and real imagery, including HI-CAMP I and II data. Performance estimates and samples of unclassified processed imagery are presented.