Aggregation of Robust Regularization with Dynamic Filtration for Enhanced Radar Imaging
| dc.contributor.author | Shkvarko, Yuriy | |
| dc.contributor.author | Villalón-Turrubiates, Iván E. | |
| dc.contributor.author | Leyva-Montiel, José L. | |
| dc.date.accessioned | 2016-04-04T17:41:17Z | |
| dc.date.available | 2016-04-04T17:41:17Z | |
| dc.date.issued | 2006 | |
| dc.description | The paper suggest a novel approach to the problem of high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties. We explain the theory recently developed by the authors of this presentation that addresses a new fused Bayesian-regularization paradigm for radar/SAR image formation/reconstruction. We show how this theory leads to new adaptive and robustified computational methods that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk estimation strategy with the ME randomized a priori image model and other projection-type regularization constraints imposed on the solution. We detail such fused Bayesian-regularization (FBR) paradigm and analyze some efficient numerical schemes for computational implementation of the relevant FBR-based methods. Also, we present the results of extended simulation study of the family of the radar image (RI) formation algorithms that employ the proposed FBR paradigm for high-resolution reconstruction of the SSP of the wavefield sources distributed in the remotely sensed environment. The last issue that we address as a perspective innovation is a paradigm of incorporating the concept of dynamic computing into the FBR-based technique to enable the latter to reconstruct the desired environmental remote sensing signatures (RSS) extracted from the enhanced imagery taking into account their dynamical behaviour. This provides a background for understanding the future trends in development of intelligent dynamic RS imaging and resource management techniques. The advantages of the well designed RI experiments (that employ the FBR-based methods) over the cases of poorer designed experiments (that employ the matched spatial filtering as well as the constrained least squares estimators) are investigated trough the simulation study. | es |
| dc.description.sponsorship | ITESO, A.C. | es |
| dc.identifier.citation | Yuriy V. Shkvarko, Iván E. Villalón-Turrubiates y José L. Leyva-Montiel (2006). “Aggregation of Robust Regularization with Dynamic Filtration for Enhanced Radar Imaging”, Journal of Applied Radioelectronics, 5(3), pp.316-325. | es |
| dc.identifier.issn | 1727-1290 | |
| dc.identifier.uri | http://hdl.handle.net/11117/3228 | |
| dc.language.iso | eng | es |
| dc.publisher | Kharkiv National University of Radio Electronics | es |
| dc.relation.ispartofseries | Journal of Applied Radioelectronics;5(3) | |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf | es |
| dc.subject | Robust Regularization | es |
| dc.subject | High-resolution | es |
| dc.subject | Dynamic Filtration | es |
| dc.subject | Enhanced Radar Imaging | es |
| dc.title | Aggregation of Robust Regularization with Dynamic Filtration for Enhanced Radar Imaging | es |
| dc.type | info:eu-repo/semantics/article | es |
| rei.peerreviewed | Yes | es |
| rei.revisor | Journal of Applied Radioelectronics |
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