Direction-of-Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation

Liu, Jianfei; Wu, Xiongbin; Emery, William J.; Zhang, Lan; Li, Chuan; Ma, Kewei

doi:10.1109/lsp.2016.2632750

Cited by 23 publications

(14 citation statements)

References 14 publications

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“…In this method, the sensor locations are assumed to be known, and the gain and phase calibration errors of the sensors are estimated. Similarly, the signal estimation and the blind sensor calibration are performed separately in [32]. In [33], a blind calibration method using independent component analysis is studied which considers the gain and the phase errors for a linear array.…”

Section: Application To Direction Of Arrival Estimationmentioning

confidence: 99%

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: Application To Direction Of Arrival Estimationmentioning

confidence: 99%

Sparsity based off-grid blind sensor calibration

Çamlıca

Yetik

Arıkan

2019

Digital Signal Processing

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“…Gain-phase error calibration is discussed in [10]- [14], while mutual coupling calibration is investigated in [15]- [18]. All of these imperfections are regarded in a unified manner in [19]- [21], comprehensively.…”

Section: Joint Doa Estimation and Array Calibrationmentioning

confidence: 99%

Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning

Ghanbari¹,

Zayyani²,

Yazdian³

2017

Preprint

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This letter proposes a multiple parametric dictionary learning algorithm for direction of arrival (DOA) estimation in presence of array gain-phase error and mutual coupling. It jointly solves both the DOA estimation and array imperfection problems to yield a robust DOA estimation in presence of array imperfection errors and off-grid. In the proposed method, a multiple parametric dictionary learning-based algorithm with an steepest-descent iteration is used for learning the parametric perturbation matrices and the steering matrix simultaneously. It also exploits the multiple snapshots information to enhance the performance of DOA estimation. Simulation results show the efficiency of the proposed algorithm when both off-grid problem and array imperfection exist.

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“…The covariance matrix does not contain the phase error, so the subspace algorithm can be directly used to estimate the direction of the signal source. In References [ 37 , 38 ], the relationship between the gain-phase errors and the array manifold vector is established to eliminate the errors in MUSIC spatial spectrum, Then the DOA of two calibration sources is estimated by two-dimensional spatial spectrum search. The algorithms in References [ 35 , 36 , 37 , 38 ] do not need iteration, and the estimation performance is independent of the phase error.…”

Section: Introductionmentioning

confidence: 99%

Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation

Dai

2020

Sensors

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In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm.

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Direction-of-Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation

Cited by 23 publications

References 14 publications

Sparsity based off-grid blind sensor calibration

Sparsity based off-grid blind sensor calibration

Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning

Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation

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