A Novel Robust Adaptive Beamformer using Diagonal Loading

Abstract

beamfomer which utilizes MVDR beamformer along with SMI (sample matrix inversion), actual data is not available to calculate the covariance matrix. Instead, covariance matrix is estimated from the available data. It may result in bad conditioning. To avoid this, diagonal elements are introduced in the correlation matrix, which is called diagonal loading. Diagonal loading can be inserted by adding a scaled version of identity matrix to impart Robustness to the adaptive beamformer. This proves to be efficient against signal mismatch due to low sample support and helps to achieve desired sidelobe level and SINR improvement. A novel hybrid algorithm for MVDR(SMI beamformer with colored adaptive diagonal loading is proposed in this paper. The performance of the proposed method is compared with other methods such as Conventional, MVDR(SMI(Diagonal Loading, MVDR(SMI( Colored -DL, MVDR(SMI(Adaptive DL by conducting simulation experiments to prove its effectiveness in improving the directivity and SINR degradation in performance. The SINR which is a measure of performance of the beamformer degrades as sample support (the number of data) is low. The lower band on sidelobe levels of the beamformer when no interference sources are found at an angle is also calculated. Training issues like the presence of desired signal in the correlation matrix n i R + is also dealt with. The paper is organized as follows. In Section 2, Problem formulation and general model is presented. In Section 3 Adaptive beamforming with various beamforming methods are presented along with the Novel Hybrid algorithm (Adaptive colored diagonal loading. In Section 4 simulation experiments are presented. Section 5 contains Results and discussions. Section 6 presents the conclusions.