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Minimum variance beamformer (MVB) has a high computational complexity that is mainly due to the inversion of an L×L covariance matrix involved during weight vector estimation, where L is the length of the subarray. In this work an attempt is made to reduce the computational complexity as well as increase the robustness against signal mismatch. The computational complexity is reduced by projecting the element-space data on to beamspace domain and then using dominant mode rejection on the beamspace covariance matrix (BCM). This reduces the dimension of covariance matrix and also eliminates the matrix inversion thereby reducing the computational complexity. Further, a closeness factor is introduced to determine the interference components that have to be suppressed, leading to increased robustness against signal mismatch. Performance of the proposed method has been evaluated on both simulated and experimental datasets. Results indicate that the proposed beamformer has a lateral resolution of 0.07 mm and a contrast resolution of 0.80, which are comparable to that of MVB which has a lateral and contrast resolution of 0.10 mm and 0.78, that too with a 12-fold reduction in computational complexity. The robustness of the peak magnitude estimate and spatial resolution of the beamformer with respect to error in estimation of sound velocity in the medium have also been evaluated. The variation in lateral resolution of proposed beamformer and MVB is approximately 1.21 mm and 1 mm. Further, the proposed beamformer has a maximum deviation in peak magnitude estimate of 0.7 dB whereas that of MVB is 2.5 dB, thus indicating the increased robustness of proposed method in peak magnitude estimate. Overall, the proposed beamformer has a 12-fold lower computational complexity compared to MVB with additional flexibility to increase the robustness against error in sound velocity.
This article was published in the following journal.
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