Self-Calibration Method of Sensors Array Errors Based on Rotation Measurement

Array calibration is the premise of successful array signal processing. The active calibration needs to know the direction of arrival (DOA) of calibration source signals accurately; otherwise, the performance will deteriorate. Although the conventional self-calibration does not need accurate DOA of calibration sources, it may suffer from falling into the local extremum since there are too many parameters to be estimated. Thus, we propose a novel self-calibration method based on the rotation measurement. The rotation measurement is that the array receives signals from a fixed source with different array rotation angles. The proposed method jointly estimates array errors and DOAs of the sources by an alternative minimization (AM) algorithm. Through the derivation of a suitable initial value in the AM algorithm, we could improve the robustness and reduce the computational complexity. The proposed method can achieve as high calibration accuracy as the active method even if there is an error in the source DOA information and can apply to arbitrary planar arrays. Both simulation and field experiment results demonstrate the effectiveness of the proposed method.

Automated summary

Array calibration is crucial for successful array signal processing. Active calibration requires accurate knowledge of the direction of arrival (DOA) of calibration source signals to avoid performance degradation. A novel self-calibration method based on rotation measurement is proposed to address the issue of estimation parameters when performing conventional self-calibration. The method jointly estimates array errors and DOAs of sources through an alternative minimization (AM) algorithm, offering improved robustness and reduced computational complexity.