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The image focus quality of multiple-pass 3D SAR imagery strongly depends on the accuracy of the betweenpass coherent image alignment. We present a method for forming a 3D SAR image from multiple coherently aligned 2D SAR images collected at different viewing geometries, where the joint coherent alignment is performed by directly optimizing the 3D SAR image entropy. Image entropy based focusing is inherently data-adaptive and imposes only minimal assumptions about the SAR imagery such as it having a non-Gaussian distribution, as opposed to interferometric based methods which typically rely on the assumption of one or a few bright scatterers per resolution cell. We will show examples of coherently aligning and focusing 3D SAR imagery using both simulated as well as measured multiple-pass SAR imagery.

Previously we introduced the Uniform Cram&-Rao (CR) Bound as a lower bound on the variance of biased estimators, along with the concept of the delta-sigma tradeoff curve. For an estimator whose variance lies on this curve, lower variance can only be achieved at the price of increased estimator bias gradient norm, and vice versa.However, for single pixel estimation, one can specify a variety of different estimator point response functions that have identical bias-gradient norm but with widely different resolution properties. This has lead to some counter-intuitive results and interpretation difficulties when using the Uniform CR Bound in performance studies of imaging systems.We now extend this tradeoff concept by introducing the 2nd-moment of the point response function as a measure of resolution for single-pixel estimation tasks. We derive an expression for the delta-gamma-sigma tradeoff surface. This surface specifies an "unachievable region" of estimator variance. For estimators that lie on this surface, lower variance can only be achieved at the price of increased bias gradient norm and/or decreased estimator resolution. We present a method for computing this surface for linear gaussian inverse problems.