A Calibration Method for Linear Arrays in the Presence of Gain-Phase Errors

Dai, Zheng; Su, Weimin; Gu, Hong

doi:10.1587/transfun.2019eal2160

Cited by 2 publications

(8 citation statements)

References 12 publications

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“…In this paper, we compare the computational complexity of the proposed method, the method in Reference [ 40 ] and the EACDM method. Suppose that the number of array elements is M , the number of sources is D , the number of snapshots is L , and the number of search points is N .…”

Section: Discussionmentioning

confidence: 99%

“…Suppose that the number of array elements is M , the number of sources is D , the number of snapshots is L , and the number of search points is N . Then, the computational complexity of the proposed method is

, the computational complexity of the method in Reference [ 40 ] can be expressed as

, and the computational complexity of the EACDM method can be described as

. It can be seen that the EACDM method has the lowest computational complexity.…”

Section: Discussionmentioning

confidence: 99%

“…It can be seen that the EACDM method has the lowest computational complexity. The computational complexity of the proposed algorithm is more affected by the number of sources, while the method in Reference [ 40 ] is more affected by the number of array elements. However, the proposed method should have better performance than the EACDM method and the method in Reference [ 40 ] at low SNR.…”

Section: Discussionmentioning

confidence: 99%

“…The computational complexity of the proposed algorithm is more affected by the number of sources, while the method in Reference [ 40 ] is more affected by the number of array elements. However, the proposed method should have better performance than the EACDM method and the method in Reference [ 40 ] at low SNR. The EACDM method uses approximate calculations in the derivation process, but this approximation will have a large deviation when the SNR is lower than 10dB.…”

Section: Discussionmentioning

confidence: 99%

“…The EACDM method uses approximate calculations in the derivation process, but this approximation will have a large deviation when the SNR is lower than 10dB. The method in Reference [ 40 ] performs eigen-decomposition of the array covariance matrix during the derivation process to separate the signal subspace and the noise subspace. Under the low SNR, the separation effect will be worse.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

, the computational complexity of the method in Reference [ 40 ] can be expressed as

, and the computational complexity of the EACDM method can be described as

. It can be seen that the EACDM method has the lowest computational complexity.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Dai

2020

Sensors

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Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

et al. 2021

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Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

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A Calibration Method for Linear Arrays in the Presence of Gain-Phase Errors

Cited by 2 publications

References 12 publications

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

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

Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

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