Maximum entropy regularization in inverse synthetic aperture radar imagery

The method of maximum entropy is applied to the regularization of inverse synthetic aperture radar (ISAR) image reconstructions. This is accomplished by considering an ensemble of images with associated `allowed´ probability density functions. Instead of directly considering the `solution´ to be an image, the author takes it to be the a posteriori probability density found by minimizing a regularization functional composed of the usual `least squares´ term and a Kullback (cross-entropy) information difference term. The desired image is then found as the expectation of this density. The basic model of this approach is similar to that used in usual maximum a posteriori analysis and allows for a more general relationship between the image and its `configuration entropy´ than is usually employed. In addition, it eliminates the need for inappropriate nonnegativity constraints on the (generally complex-valued) image