Abstract
We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.
Highlights
Space-time adaptive processing (STAP) for high-resolution radar imaging with sensor arrays and synthetic aperture radar (SAR) systems has been an active research area in the environmental remote sensing (RS) field for several decades, and many sophisticated techniques are available
The problem of radar/SAR imaging can be formalized in terms of nonlinear inverse problems of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from the remotely sensed scene observed through a kernel signal formation operator (SFO) with the kernel structure specified by the employed radar/SAR
We show how the proposed dynamic experiment design (DYED) regularization approach may be considered as a generalization of the APES [24], and some other novel high-resolution “model-based” nonparametric radar imaging techniques [15,27], on one hand, and the variational analysis (VA)-related anisotropic diffusion [16,18], selective anisotropic information fusion [20] and other nonparametric “model-free” robust adaptive beamforming based image enhancement approaches [28,29,30,31,32,33], on the other hand
Summary
Space-time adaptive processing (STAP) for high-resolution radar imaging with sensor arrays and synthetic aperture radar (SAR) systems has been an active research area in the environmental remote sensing (RS) field for several decades, and many sophisticated techniques are available (see among others [1,2,3,4] and the references therein). Related methods that fall into the category of the so-called “blind” or “model-free” image enhancement approaches have recently been adapted to RS image enhancement, e.g., [16,17,18,19,20] but without their aggregation with the resolution enhancing “model-based” nonparametric regularized imaging techniques [21,22,23,24] Another possible way to alleviate the ill-posedness of the nonlinear radar/SAR imaging problems is to incorporate a priori model considerations regarding the desired geometrical scene image properties into the STAP procedures via performing randomization of the SSP model and application of the Bayesian minimum risk (MR) or maximum a posteriori probability (MAP) nonparametric adaptive spatial spectral estimation strategies [3,21]. POCS regularization unified with the DEED paradigm that results in a new proposed DYED framework is addressed in Section 5 followed by some illustrative simulations and discussion in Sections 6 and conclusions in Section 7, respectively
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