A Canonical Framework for Suboptimum Space-Time Adaptive Processing (STAP), Including Covariance-Matrix Estimation and Range-Dependence Compensation
نویسندگان
چکیده
We address the problem of detecting slow moving targets from a moving radar system using spacetime adaptive processing (STAP) techniques. Optimum interference rejection requires the estimation and the inversion of an interference-plus-noise covariance matrix. To reduce the number of samples involved in the estimation and the computational cost inherent to the inversion, many suboptimum STAP techniques have been proposed. We propose a new canonical framework that encompasses all suboptimum STAP methods we are aware of. In addition, the framework allows for both covariance-matrix estimation and range-dependence compensation. This framework thus applies to both monostatic and bistatic configurations.
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