Blind calibration of phased arrays using sparsity constraints on the signal model

Wijnholds, Stefan J.; Chiarucci, Simone

doi:10.1109/eusipco.2016.7760252

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…l r l , which can be used in the following iterative solution of the optimization problem in the Eq. 8: (12) where μ p,l is the properly chosen step size parameter. The value of the step size can be chosen as a sufficiently small ratio of the initial grid size of the OMP structure given in Table 1.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Sparsity based off-grid blind sensor calibration

Çamlıca

Yetik

Arıkan

2019

Digital Signal Processing

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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.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…In [11], sparsity based blind calibration of sensor networks is studied using total least squares approach. Sparsity based blind sensor calibration is also studied in [12] for phased array sensors. In [13], a moments based blind calibration method is studied for mobile sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Sparsity based off-grid blind sensor calibration

Çamlıca

Yetik

Arıkan

2019

Digital Signal Processing

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“…These methods become computationally impractical with huge numbers of observations, making them unsuitable for calibration in the radio interferometer context for which we commonly access only the sample covariance matrix rather than the time signal itself [7] . This issue was also recognized in recently in [22,41] , in which the authors propose a technique for, respectively, full calibration and blind calibration of the DI gains for individual frequency channels by assuming that the observed scene is sparse. We stress that many works based of compressed sensing has been developed for image reconstruction in the radio astronomy community [42][43][44][45] as a result of the sparse nature of the interferometric sampling.…”

Section: Introductionmentioning

confidence: 93%

Parallel multi-wavelength calibration algorithm for radio astronomical arrays

Brossard¹,

Korso²,

Pesavento³

et al. 2018

Signal Processing

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International audienceIn order to meet the theoretically achievable imaging performance, calibration of modern radio interferometers is a mandatory challenge, especially at low frequencies. In this perspective, we propose a novel parallel iterative multi-wavelength calibration algorithm. The proposed algorithm estimates the apparent directions of the calibration sources, the direction dependent and direction independent complex gains of the array elements and their noise powers. Furthermore, the algorithm takes into account the specific variation of the aforementioned parameter values across wavelength. Numerical simulations reveal that the proposed scheme outperforms the mono-wavelength calibration scheme and approaches the derived constrained Cramér–Rao bound even with the presence of non-calibration sources at unknown directions. Finally, simulation results on LOFAR real data assess the usefulness of the proposed scheme

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“…Recently, CS has also been applied to calibration. In [6], [27]- [29], sparsity was exploited to calibrate complex gains using targets under unknown angles. Mutual coupling was considered for on-line calibration in [30] and [31].…”

Section: Introductionmentioning

confidence: 99%

Antenna Array Calibration Using a Sparse Scene

Geiß

Sippel

Hehn

et al. 2021

IEEE Open J. Antennas Propag.

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In radar systems, antenna arrays acquire direction-dependent information to localize targets or create images of the environment. However, because of unknown complex amplitudes per channel and mutual coupling, a calibration is necessary for good performance. Common calibration approaches measure to targets under known angles in a multipath-free far-field environment, which often can only be provided by anechoic chambers. Therefore, this process is not suitable for low-cost and frequent calibration. To overcome these limitations, this paper proposes a novel calibration approach using an unknown but sparse target scene. Multiple measurements at known relative positions of the radar are combined to a synthetic aperture. Then, the full mutual coupling matrix is estimated simultaneously with the unknown target scene. The method requires neither target position information, far-field conditions, nor an anechoic chamber because multipath propagation can be suppressed easily for targets located in the near-field. The proposed calibration approach is validated by measurements of a commercial 77 GHz radar. The performance is evaluated by comparing the achieved image quality using the calibration results of this work and the radar's ex-factory calibration data. The proposed novel calibration procedure improves the image quality, while considerably lowering the demands on the calibration measurements and environment.

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Blind calibration of phased arrays using sparsity constraints on the signal model

Cited by 9 publications

References 9 publications

Sparsity based off-grid blind sensor calibration

Sparsity based off-grid blind sensor calibration

Parallel multi-wavelength calibration algorithm for radio astronomical arrays

Antenna Array Calibration Using a Sparse Scene

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