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Title: Sparsity based off-grid blind sensor calibration
Authors: Çamlıca, Sedat
Yetik, İmam Şamil
Arıkan, Orhan
Keywords: Blind calibration
compressive sensing
direction finding
frequency estimation
Issue Date: Jun-2019
Publisher: Elsevier Inc.
Source: Camlica, S., Yetik, I. S., and Arikan, O. (2019). Sparsity based off-grid blind sensor calibration. Digital Signal Processing, 84, 80-92.
Abstract: Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal has a sparse support restricted on the discretized grid points. This restriction of representing the signal on a discretized grid results in the off-grid problem which causes performance degradation in the reconstruction of signals. Sensor calibration is another issue which can cause performance degradation if not properly addressed. Calibration aims to reduce the disruptive effects of the phase and the gain biases. In this paper, a CS based blind calibration technique is proposed for the reconstruction of multiple off-grid signals. The proposed technique is capable of estimating the off-grid signals and correcting the gain and the phase biases due to insufficient calibration simultaneously. It is applied to off-grid frequency estimation and direction finding applications using blind calibration. Extensive simulation analyses are performed for both applications. Results show that the proposed technique has superior reconstruction performance. (C) 2018 Elsevier Inc. All rights reserved.
ISSN: 1051-2004
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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