Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors

LI Cunxu; CHEN Baixiao

doi:10.11999/JEIT161318

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2219-2224

LI Cunxu, CHEN Baixiao. Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2219-2224. doi: 10.11999/JEIT161318

Citation:

LI Cunxu, CHEN Baixiao. Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2219-2224. doi: 10.11999/JEIT161318

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Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors

doi: 10.11999/JEIT161318

LI Cunxu¹,
CHEN Baixiao¹

Funds:

The National Natural Science Foundation of China (61571344)

Received Date: 2016-12-08
Rev Recd Date: 2017-03-30
Publish Date: 2017-09-19

Abstract

Abstract

For calibration of direction-dependent gain-phase errors, with a few precisely calibrated instrumental sensors, a method that jointly estimates the direction-dependent gain-phase errors and the target azimuth by spatial sparsity of the signal is proposed. The array manifold that perturbed by direction-dependent gain-phase errors is denoted by the multiplication form of ideally array manifold and a gain-phase errors coefficient matrix, then the received signal is represented by sparse form. The calibration for gain-phase error problem is formulated as a dual optimization problem, through alternating iterative optimization method to acquire the optimal solution of the two optimization variables, so as to realize the signal incident angle and azimuth dependent amplitude and phase errors of the optimized calculation. In this paper, the proposed algorithm has better performance than the existing algorithm, performance of the proposed algorithm is approximate to the Cramer-Rao low bound. The simulation experiments verify the effectiveness and superiority of the proposed algorithm.
- Array calibration,
- Direction of arrive estimation,
- Spatial sparsity

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References(20)

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