Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

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.

Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming

“…Traditional RAB techniques include the diagonal loading (DL) technique and the eigenspace-based (ESB) technique [3,6]. The DL-based beamformers are derived by adding a scaled identity matrix on the sample covariance matrix (SCM) [3,7].…”

“…The uncertain-set-based technique utilizes a presumed spherical or ellipsoidal uncertainty set to constrain the signal SV mismatches and correct the nominal SV by solving an optimization problem [8]. The uncertain-set-based beamformers roughly include the following methods [6]: worst-case-based method [1,16], doubly constrained method [9,17], probabilistically constrained method [10,11] and linear programming method [18]. Compared with traditional DL methods, uncertainty-set-based methods are proposed based on clear theoretical analysis and thus have better robustness.…”

“…The above methods mainly focus on correcting signal SV and enhancing the covariance matrix [21]. Since the desired signal component in the SCM is not eliminated [22], the above methods always suffer from performance degradation at high input SNRs [6]. To avoid this issue, the authors in [23] pioneer a novel RAB method called INCM reconstruction based method.…”

“…This method has been proved to effectively eliminate the desired signal components. Gu modifies the above method in [5] and improves the performance, where the INCM is reconstructed by integrating over the complement of the desired signal angular region [6]. Unfortunately, this method performs poorly when the array calibration error exists in the systematic model, and the complexity of this method is very high.…”

“…For reducing INCM reconstruction errors, the residual noise is considered and eliminated from the Capon power spectrum estimator to reconstruct INCM and the desired signal SV more precisely in [7]. It is true that the incident signal may be covered by noise in the Capon power spectrum when the SV mismatch is large enough [6]. To overcome this drawback, the methods proposed in [26,27] substitute the Capon power spectrum with the maximum entropy power spectrum.…”