Abstract
Recently, ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging has been proposed for pipeline penetrating radar applications thanks to its capability in providing suitable resolution and penetration depth. Because of geometrical restrictions, there are many complicated sources of clutter in the pipe. However, this issue has not been investigated yet. In this article, we investigate some well-known clutter removal algorithms using full-wave simulated data and compare their results considering image quality, signal to clutter ratio and contrast. Among candidate algorithms, two-dimensional singular spectrum analysis (2-D SSA) shows a good potential to improve the signal to clutter ratio. However, basic 2-D SSA produces some artifacts in the image. Therefore, to mitigate this issue, we propose “modified 2-D SSA.” After developing the suitable clutter removal algorithm, we propose a complete algorithm chain for pipeline imaging. An UWB near-field SAR monitoring system including an UWB M-sequence sensor and automatic positioner are implemented and the image of drilled perforations in a concrete pipe mimicking oil well structure as a case study is reconstructed to test the proposed algorithm. Compared to the literature, a comprehensive near-field SAR imaging algorithm including new clutter removal is proposed and its performance is verified by obtaining high-quality images in experimental results.
Highlights
N OWADAYS, pipeline penetrating radars (PPR) has been considered for pipeline condition assessment in various areas such as water pipeline, asbestos cement pipeline in sewer pipeline, gas pipelines, etc
Ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging as a PRP has found a new application in Manuscript received February 1, 2020; accepted March 15, 2020
Size of the production zone casing depends on AKBARPOUR et al.: CLUTTER REMOVAL OF NEAR-FIELD UWB SAR IMAGING FOR PIPELINE PENETRATING RADAR
Summary
N OWADAYS, pipeline penetrating radars (PPR) has been considered for pipeline condition assessment in various areas such as water pipeline, asbestos cement pipeline in sewer pipeline, gas pipelines, etc. Due to the complexity of the medium and imaging in the near-field, oil well monitoring like other pipeline monitoring applications faces various sources of clutter that must be reduced to monitor the perforations condition. The classical filtering method of mean subtraction is a good candidate for Tx/Rx cross talk and extracting background signal Another source is scattering from neighboring unevenly distributed objects in the scene. After preliminary investigation of the nominated methods, we propose a signal processing chain to reconstruct the image of perforations drilled in oil well wall.
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