High-Resolution Recovery of Spatial Reflectivity Maps in Harsh Remote Sensing Scenarios A Metrically Structured Experiment Design Regularization Approach

摘要

A new robust experiment design (RED) descriptive spatial spectral estimation approach for high-resolution recovery of the spatial reflectivity maps is addressed as required for radar imaging in harsh remote sensing (RS) scenarios. The harsh operational uncertainties are attributed for possible imperfect sensor calibration, unknown a priori image statistics and carrier trajectory deviations peculiar to all conventional low resolution side looking radar sensors. To achieve a high-resolution RS image recovery, we propose to aggregate the RED strategy with the variational analysis (VA) inspired ?_2-?_1 metrics structured regularization. The latter employs the prior model about the piecewise sparseness of the scene reflectivity gradient maps that alleviates the overall imaging inverse problem ill-posedness. The fused RED-VA structured sensing method implemented in an efficient iterative computing mode outperforms the most prominent competing radar imaging technique that do not aggregate the RED with the ?_2-?_1 metrically structured VA in the considered harsh RS operational scenarios as verified in the reported numerical simulations.