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Author Shkvarko, Yuriy ♦ Tuxpan, José ♦ Santos, Stewart
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Computer science, information & general works ♦ Library & information sciences ♦ Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Technology ♦ Medicine & health ♦ Human physiology
Subject Domain (in MeSH) Mathematical Concepts ♦ Biological Sciences ♦ Information Science ♦ Information Science
Subject Keyword Discipline Biotechnology ♦ Radar ♦ Algorithms ♦ Image Processing, Computer-assisted ♦ Journal Article
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.
Description Author Affiliation: Shkvarko Y ( Department of Telecommunications of Center of Research and Advanced Studies of I.P.N., Av. del Bosque 1145, Col. El Bajío, Zapopan, Jalisco, C.P. 45019, México.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-01-01
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 11
Issue Number 5

Source: WHO-Global Index Medicus