27 August 2001 Efficient implementation of an expectation-maximization algorithm for imaging diffuse radar targets
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We explore a statistical view of radar imaging in which target reflectances are realizations of an underlying random process. For diffuse targets, this process is zero-mean complex Gaussian. The data consists of a realization of this process, observed through a linear transformation, and corrupted by additive noise. Image formation corresponds to estimating the elements of a diagonal covariance matrix. In general, maximum-likelihood estimates of these parameters cannot be computed in closed form. Snyder, O'Sullivan, and Miller proposed an expectation-maximization algorithm for computing these estimates iteratively. Straightforward implementations of the algorithm involve multiplication and inversion operations on extremely large matrices, which makes them computationally prohibitive. We present an implementation which exploits Strassen's recursive strategy for matrix multiplication and inversion, which may make the algorithm feasible for image sizes of interest in high-resolution radar applications.
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Aaron D. Lanterman, Aaron D. Lanterman, } "Efficient implementation of an expectation-maximization algorithm for imaging diffuse radar targets", Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438238; https://doi.org/10.1117/12.438238

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