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Abstract
Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.
Highlights
The signal of interest (SOI) steering vector (SV) was estimated by employing the iterative mismatch approximation method presented in [29]
interference–noise covariance matrix (INCM) reconstruction methods, we present a blocking matrix based on the matrix filter principle in [36,37], which is suitable for suppressing signals illuminating within a specific angular region
We proposed a low-complexity robust adaptive beamforming (RAB) method based on INCM reconstruction via subspace projection
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. These methods perform poorly in the presence of large SV mismatch and high-input SNRs. The uncertain-set-based technique utilizes a spherical or ellipsoidal uncertainty constraint setting on the nominal SV to estimate the SOI SV including the worst-case-based (WCB) method [5,18], doubly constrained method [10,19], probabilistically constrained method [20,21], and linear programming method [22]. The above methods are mainly aimed at estimating SOI SV or SCM These methods can improve the robustness of a beamformer, all still suffer from serious performance degradation at high-input SNRs. In order to overcome this drawback, a new type of RAB method based on INCM reconstruction has been developed in recent years.
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