Robust Direction-Finding Method for Sensor Gain and Phase Uncertainties in Non-uniform Environment

Yang, Long; Yang, Yixin; Liao, Guisheng; Wang, Yong

doi:10.1007/s00034-019-01237-4

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…In the current self-calibration algorithms, an iteration-based strategy is commonly used, in which the DOAs and sensor phase are alternatively estimated during the iterations [12][13][14][15][16]. However, the local minima are easily converged due to the poor initialization.…”

Section: Comparison With the Current Self-calibration Algorithmsmentioning

confidence: 99%

“…Sensor phase responses can be modeled as direction-independent (DI) [11][12][13][14][15][16][17][18][19][20][21][22][23][24] or direction-dependent (DD) parameters [25][26][27]. Although numerous self-calibration methods for an unknown sensor phase have been proposed, most of them handle the DI sensor phase.…”

Section: Introductionmentioning

confidence: 99%

“…The self-calibration methods for the DI model can be divided into two categories: self-calibration for the uncalibrated arrays and partly calibrated arrays. In the former, an iteration-based strategy is applied, and the sensor responses and DOAs are alternately estimated [12][13][14][15][16]. However, the iteration can easily converge to local minima due to poor initialization.…”

Section: Introductionmentioning

confidence: 99%

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Self-Calibration for Sparse Uniform Linear Arrays with Unknown Direction-Dependent Sensor Phase by Deploying an Individual Standard Sensor

2022

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Calibration of the unknown direction-dependent (DD) sensor phase and aliasing-free directions of arrival (DOA) estimation for sparse linear arrays are difficult tasks. In this work, we deploy an individual standard sensor with a known sensor phase response along the axis of the uncalibrated sparse linear array, a self-calibration method is proposed, in which the unknown DD sensor phase and the aliasing-free DOAs are both estimated. The proposed method is realized with a two-step scheme. In the first step, the sensor phase is eliminated by the Kronecker product of the covariance matrices in two different frequency bins, and the frequency difference satisfies the spatial Nyquist sampling theorem. Then, the DOAs can be robustly estimated without the influences of grating lobes and unknown sensor phase parameters. In the second step, the steering matrix is estimated with the known phase parameters of the deployed standard sensor. Then, the DD sensor phase is extracted from the steering matrix using the DOAs obtained in the first step. Hence, the disadvantages of iteration-based strategies in conventional calibration algorithms (e.g., local minima convergence) can be avoided. The performance of the proposed method is evaluated using simulation data and compared with that of Cramer–Rao bounds.

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