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The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar's signal model is presented, which allows for estimating scattering targets' vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method's performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances. [ABSTRACT FROM AUTHOR]

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography has shown great potential in multi-dimensional monitoring of urban infrastructures and detection of their possible slow deformations. Along this line, undeniable improvements in SAR tomography (TomoSAR) detection framework of multiple permanent scatterers (PSs) have been observed by the use of a multi-looking operation that is the necessity for data's covariance matrix estimation. This paper attempts to further analyze the impact of a robust multi-looking operation in TomoSAR PS detection framework and assess the challenging issues that exist in the estimation of the covariance matrix of large stack data obtained from long interferometric time series acquisition. The analyses evaluate the performance of non-local covariance matrix estimation approaches in PS detection framework using the super-resolution multi-looked Generalized Likelihood Ratio Test (GLRT). Experimental results of multi-looking impact assessment are provided using two datasets acquired by COSMO-SkyMED (CSK) and TerraSAR-X (TSX) over Tehran, Iran, and Toulouse, France, respectively. The results highlight that non-local estimation of the sample covariance matrix allows revealing the presence of the scatterers, that may not be detectable using the conventional local-based framework. [ABSTRACT FROM AUTHOR]