Finite Array Observations-Adapted Regularization Unified with Descriptive Experiment Design Approach for High-Resolution Spatial Power Spectrum Estimation with Application to Radar/SAR Imaging

摘要

We address a new approach to solving array radar/SAR imaging problems stated and treated as uncertain ill-posed inverse problems of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene via processing the discrete measurements of a finite number of independent observations of the degraded data signals (one realization of the trajectory signal in the case of SAR). The problem is treated in the framework of the Worst-case statistical performance Optimization-adapted Regularization (WOR) method aggregated with Descriptive Experiment Design (DED) paradigm. Our approach is based on the optimization of worst-case statistical performance of the resulting finite-dimensional fused WORDED estimator of the SSP. The DED-formalized projection schemes as well as the weighting "degrees of freedom" of the WOR strategy are incorporated into the optimization procedure subject to the statistical operational worst-case performance constraints imposed on the desired solution operator.