Pietro Milillo, Deodato Tapete, Francesca Cigna, Daniele Perissin, Jacqueline Salzer, Paul Lundgren, Eric Fielding, Roland Burgmann, Filippo Biondi, Giovanni Milillo, Carmine Serio
Structural health monitoring (SHM) of engineered structures consists of an automated or semi-automated survey system that seeks to assess the structural condition of an anthropogenic structure. The aim of an SHM system is to provide insights into possible induced damage or any inherent signals of deformation affecting the structure in terms of detection, localization, assessment, and prediction. During the last decade there has been a growing interest in using several remote sensing techniques, such as synthetic aperture radar (SAR), for SHM. Constellations of SAR satellites with short repeat time acquisitions permit detailed surveys temporal resolution and millimetric sensitivity to deformation that are at the scales relevant to monitoring large structures. The all-weather multi-temporal characteristics of SAR make its products suitable for SHM systems, especially in areas where in situ measurements are not feasible or not cost effective. To illustrate this capability, we present results from COSMO-SkyMed (CSK) and TerraSAR-X SAR observations applied to the remote sensing of engineered structures. We show how by using multiple-geometry SAR-based products which exploit both phase and amplitude of the SAR signal we can address the main objectives of an SHM system including detection and localization. We highlight that, when external data such as rain or temperature records are available or simple elastic models can be assumed, the SAR-based SHM capability can also provide an interpretation in terms of assessment and prediction. We highlight examples of the potential for such imaging
capabilities to enable advances in SHM from space, focusing on dams and cultural heritage areas.
SAR Tomography is the extension of the conventional interferometric radar signal processing, extended in the
height dimension. In order to improve the vertical resolution with respect to the classical Fourier methods, high
resolution approaches, based on the Convex Optimization (CVX), has been implemented. This methods recast in the
Compressed Sensing (CS) framework that optimize tomographic smooth profiles via atomic decomposition, in order
to obtain sparsity. The optimum solution has been estimated by Interior Point Methods (IPM). The problem for such
kind of signal processing is that the tomographic phase information may be suppressed and only the optimized
energy information is available. In this paper we propose a method in order to estimate an optimized spectra and
phase information projecting each vector components of each tomographic resolution cell spanned in the real and
the imaginary component. The tomographic solutions has been performed by processing multi-baseline SAR
datasets, in a full polarimetric mode, acquired by a portable small Continuous Wave (CW) radar in the X band.
In Multi-Baseline SAR tomography it is necessary to process the acquired data by advanced signal
processing techniques in order to adequately compensate the bad consequences of an under-sampled
configuration. These techniques have to properly work on an environment characterized to have point targets,
distributed targets and both of theme. This paper considers the Convex Optimization (CVX) tomographic
solution in order to process multi-baseline data-sets collected in a Fourier under-sampled configuration in the
above mentioned environment. The CVX and the Second Order Cone Programming Solution (SOCPs) have
been tested by a generic log-barrier algorithm, through a successfully computational bottleneck Newton
calculation. These techniques are validated on point targets, distributed targets and realistic forested
environments.
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