Robust beam forming method based on auxiliary array elements

Abstract

The invention provides a robust beam forming method based on an auxiliary array element, and the method comprises the steps: introducing a small number of accurately corrected auxiliary array elements into an original array with an array element position disturbance error, and achieving the decoupling processing of an array element position error parameter and a signal incoming wave direction through a subspace thought. Therefore, a more accurate expected signal steering vector and an array flow pattern are obtained, a noise power value is estimated by using a sampling covariance matrix characteristic decomposition method, and then reconstruction of an interference and noise covariance matrix is realized with low complexity based on a signal subspace fitting method. According to the method, the problem of expected signal self-cancellation can be effectively relieved, the beam forming robustness when error mismatch exists is effectively improved, and the phenomenon that the performance of a beam former is degraded due to array errors is effectively reduced; the method can effectively solve the problem of expected signal self-cancellation of a traditional robust beam forming method, is excellent in performance in an application scene with a high input signal-to-noise ratio, and effectively reduces the calculation complexity of interference and noise covariance matrix reconstruction.

Description

technical field [0001] The invention relates to the field of beamforming research in the field of array signal processing, in particular to a robust beamforming method. Background technique [0002] Adaptive beamforming is a general spatial filtering method. By weighting the received data of each array element, the main beam can be formed in the direction of the desired signal, and the null in the direction of interference can be formed to enhance the desired signal and suppress the array output. Effects of interfering signals and noise. Traditional beamforming methods are based on the ideal conditions that the desired signal steering vector and the interference plus noise covariance matrix are precisely known, and these methods are generally less robust. In practice, inaccurate, incomplete or erroneous information about the source, transmission medium, and array structure will lead to model error mismatches (such as figure 1 shown), the performance of the beamformer will ...

AI Technical Summary

Problems solved by technology

On the one hand, the reconstructed interference-plus-noise covariance matrix is ​​closer to the real interference-plus-noise covariance matrix, which can effectively alleviate the problem of self-cancellation of the desired signal; on the other hand, the steering vector of the desired signal based on the auxiliary array element method can offset the array error , which can effectively improve the robustness of beamforming in the presence of error mismatch

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