| dc.contributor.author | Shkvarko, Yuriy | |
| dc.contributor.author | Villalón-Turrubiates, Iván E. | |
| dc.date.accessioned | 2016-04-22T22:08:07Z | |
| dc.date.accessioned | 2023-03-21T15:35:34Z | |
| dc.date.available | 2016-04-22T22:08:07Z | |
| dc.date.available | 2023-03-21T15:35:34Z | |
| dc.date.issued | 2008 | |
| dc.identifier.citation | Iván E. Villalón-Turrubiates, Yuriy V. Shkvarko, “Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging”, Scientific and Technical Journal on Radioelectronics and Informatics (IJRI), vol. 1, no. 1, pp. 34-48, Marzo 2008. | es |
| dc.identifier.issn | 1563-0064 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/73316 | |
| dc.description | In this paper, we perform a comparative study of two recently proposed high-resolution radar imaging paradigms: the descriptive experiment design regularization (DEDR) and the fused Bayesian regularization (FBR) methods. The first one, the DEDR, employs aggregation of the descriptive regularization and worst-case statistical performance (WCSP) optimization approaches to enhanced radar/SAR imaging. The second one, the FBR, performs image reconstruction as a solution of the ill- conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties via unifying the Bayesian minimum risk (MR) estimation strategy with the maximum entropy (ME) randomized a priori image model and other projection-type regularization constraints imposed on the solution. Although the DEDR and the FBR are inferred from different descriptive and statistical constrained optimization paradigms, we examine how these two methods lead to structurally similar techniques that may be further transformed into new computationally more efficient robust adaptive imaging methods that enable one to derive efficient and consistent estimates of the SSP via unifying both the robust DEDR and FBR considerations. We present the results of extended comparative simulation study of the family of the image formation/ enhancement algorithms that employ the proposed robustified FBR and DEDR methods for high-resolution reconstruction of the SSP in a virtually real time. The computational complexity of different methods are analyzed and reported together with the scene imaging protocols. The advantages of the well designed SAR imaging experiments (that employ the FBR-based and DEDR-related robust estimators) over the cases of poorer designed experiments (that employ the conventional matched spatial filtering as well as the lea | es |
| dc.description.sponsorship | Cinvestav | es |
| dc.description.sponsorship | Universidad de Guadalajara | es |
| dc.language.iso | eng | es |
| dc.publisher | Kharkiv National University of Radio Electronics (KhNURE) | es |
| dc.relation.ispartofseries | Scientific and Technical Journal on Radioelectronics and Informatics (IJRI);1 | |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf | es |
| dc.subject | Bayesian Estimation | es |
| dc.subject | Maximum Entropy | es |
| dc.subject | Regularization | es |
| dc.subject | Remote Sensing | es |
| dc.subject | Spatial Spectrum Pattern | es |
| dc.subject | Sufficient Statistics | es |
| dc.subject | Radar/SAR Imaging | es |
| dc.title | Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging | es |
| dc.type | info:eu-repo/semantics/article | es |
