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Target Imaging Using Compressed Sampling in Synthetic Aperture Interferometric Radiometer

Yanyu Xu, Dong Zhu, Fei Hu, Bo Fang, Peng Fu

2023IEEE Transactions on Geoscience and Remote Sensing11 citationsDOI

Abstract

The target imaging application is significant to various sensing systems, such as radiometers, radars, and infrared. However, high system complexity impedes the application of interferometric radiometers to target imaging tasks to some extent. Specifically for an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -element interferometric radiometer with aperture synthesis technique, complex correlators are of the order of O( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), giving rise to the great difficulty of system hardware implementation. In this paper, we propose a new compressed interferometric radiometer (CIR) concept for target imaging applications, which exploits the sparsity property of targets in the spatial domain. The CIR target imaging framework mainly adopts the compressive measurement method to acquire partial visibility function samples in the spatial-frequency domain via a proper sparse sampling pattern. Then, these partially observed visibility samples are inverted to image the target contrast information by sparse recovery methods. For the above image recovery process, we propose two novel algorithms named local regional information-based reweighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> 1-norm minimization (LRRL1) and local regional convolution-based reweighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> 1-norm minimization (LRCRL1). The experiments using simulated and real data demonstrate the validity and effectiveness of the proposed CIR target imaging framework, showing superiority in both imaging performance and system complexity compared with conventional algorithms used in interferometric radiometers.

Topics & Concepts

Compressed sensingComputer scienceVisibilityArtificial intelligenceInterferometryRadiometerSampling (signal processing)Remote sensingAlgorithmSynthetic aperture radarComputer visionPhysicsGeologyOpticsFilter (signal processing)Sparse and Compressive Sensing TechniquesSoil Moisture and Remote SensingSynthetic Aperture Radar (SAR) Applications and Techniques
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