Robust DOA Estimation in the Presence of Miscalibrated Sensors

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed ap...

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Bibliographic Details
Published in:IEEE signal processing letters 2017-07, Vol.24 (7), p.1073-1077
Main Authors: Ben Wang, Zhang, Yimin D., Wei Wang
Format: Article
Language:English
Subjects:
Algorithms
Calibration error
compressive sensing (CS)
Computer simulation
Covariance matrices
Criteria
Degradation
Direction of arrival
Direction-of-arrival estimation
Estimation
maximum correntropy criterion (MCC)
outlier
Outliers (statistics)
Reconstruction
robust direction-of-arrival (DOA) estimation
Robustness
Sensor arrays
Sensors
Signal processing algorithms
Weighting
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Summary:In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2017.2708659