Abstract
We address a new approach to solve the ill-posed nonlinear inverse problem of high-resolution numerical reconstruction of the spatial spectrum pattern (SSP) of the backscattered wavefield sources distributed over the remotely sensed scene. An array or synthesized array radar (SAR) that employs digital data signal processing is considered. By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR) strategy for enhanced radar imaging. Pursuing such an approach, we establish a family of the SDR-related SSP estimators, that encompass a manifold of existing beamforming techniques ranging from traditional matched filter to robust and adaptive spatial filtering, and minimum variance methods.
Highlights
In this paper, we address a new approach to enhanced array radar or synthesized array radar (SAR) imaging stated and treated as an ill-posed nonlinear inverse problem
We address a new approach to enhanced array radar or SAR imaging stated and treated as an ill-posed nonlinear inverse problem
We propose a new fused statistical descriptive regularization (SDR) approach for estimating the spatial spectrum pattern (SSP) that aggregates the statistical minimum risk inference paradigm [2, 3] with the descriptive regularization techniques [4, 13]
Summary
The SSP is defined as a spatial distribution of the power (i.e., the second-order statistics) of the random wavefield backscattered from the remotely sensed scene observed through the integral transform operator [1, 2] Such operator is explicitly specified by the employed radar signal modulation and is traditionally referred to as the signal formation operator (SFO) [2, 3]. We propose a new fused statistical descriptive regularization (SDR) approach for estimating the SSP that aggregates the statistical minimum risk inference paradigm [2, 3] with the descriptive regularization techniques [4, 13] Pursuing such an approach, we establish a family of the robust SDR-related estimators that encompass a manifold of existing imaging techniques ranging from traditional array matched spatial filtering to high-resolution minimum variance adaptive array beamforming. The efficiency of two particular SDR algorithms (the robust spatial filtering (RSF) algorithm and the adaptive spatial filtering (ASF) algorithm) is illustrated through computer simulations of reconstructing the digital images provided with the SAR operating in some typical remote sensing scenarios
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