scholarly journals SELF-CALIBRATION ALGORITHM WITH GAIN-PHASE ERRORS ARRAY FOR ROBUST DOA ESTIMATION

2021 ◽  
Vol 99 ◽  
pp. 1-12
Author(s):  
Zhenyu Wei ◽  
Wei Wang ◽  
Fuwang Dong ◽  
Ping Liu
Keyword(s):  
Doa Estimation ◽  
Self Calibration ◽  
Phase Errors ◽  
Calibration Algorithm

scholarly journals Error Self-Calibration Algorithm for Acoustic Vector Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Mingxing Zhang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Estimation Performance ◽  
Self Calibration ◽  
Phase Errors ◽  
Vector Sensor ◽  
Calibration Algorithm ◽  
The Impact ◽  
Vector Sensor Array

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

DOA estimation and self-calibration algorithm for uniform circular array

2005 ◽  
Vol 41 (20) ◽  
pp. 1092 ◽  
Author(s):  
C. Qi ◽  
Y. Wang ◽  
Y. Zhang ◽  
H. Chen
Keyword(s):  
Doa Estimation ◽  
Circular Array ◽  
Self Calibration ◽  
Calibration Algorithm

scholarly journals Strip-Mode Microwave Staring Correlated Imaging with Self-Calibration of Gain–Phase Errors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1079 ◽  
Author(s):  
Rui Xia ◽  
Yuanyue Guo ◽  
Weidong Chen ◽  
Dongjin Wang
Keyword(s):  
Error Estimation ◽  
Phase Error ◽  
Super Resolution ◽  
Image Splicing ◽  
Initial Value ◽  
Self Calibration ◽  
Phase Errors ◽  
Imaging Model ◽  
Multiple Imaging ◽  
Imaging Performance

Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain–phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to solve the problem of MSCI with gain–phase error in a large scene, a method of MSCI with strip-mode self-calibration of gain–phase errors is proposed. The method divides the whole imaging scene into multiple imaging strips, then the strip target scattering coefficient and the gain–phase errors are combined into a multi-parameter optimization problem that can be solved by alternate iteration, and the error estimation results of the previous strip can be carried into the next strip as the initial value. All strips are processed in multiple rounds, and the gain–phase error estimation results of the last strip can be taken as the initial value and substituted into the first strip for the correlated processing of the next round. Finally, the whole imaging in a large scene can be achieved by multi-strip image splicing. Numerical simulations validate its potential advantages to shorten the imaging time dramatically and improve the imaging and gain–phase error estimation performance.

scholarly journals Sparsity-Based DOA Estimation with Gain and Phase Error Calibration of Generalized Nested Array

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ziang Feng ◽  
Guoping Hu ◽  
Hao Zhou
Keyword(s):  
Degrees Of Freedom ◽  
Phase Error ◽  
Doa Estimation ◽  
Model Errors ◽  
Multiple Sources ◽  
Sparse Arrays ◽  
Estimation Algorithms ◽  
Phase Errors ◽  
Representation Method ◽  
Cramer Rao Bound

Sparse arrays, which can localize multiple sources with less physical sensors, have attracted more attention since they were proposed. However, for optimal performance of sparse arrays, it is usually assumed that the circumstances are ideal. But in practice, the performance of sparse arrays will suffer from the model errors like mutual coupling, gain and phase error, and sensor’s location error, which causes severe performance degradation or even failure of the direction of arrival (DOA) estimation algorithms. In this study, we follow with interest and propose a covariance-based sparse representation method in the presence of gain and phase errors, where a generalized nested array is employed. The proposed strategy not only enhances the degrees of freedom (DOFs) to deal with more sources but also obtains more accurate DOA estimations despite gain and phase errors. The Cramer–Rao bound (CRB) derivation is analyzed to demonstrate the robustness of the method. Finally, numerical examples illustrate the effectiveness of the proposed method from DOA estimation.

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