An Approach to a Low Dimensional Covariance Matrix for Minimum Variance Beamforming

The beamforming techniques greatly reduce the contribution of undesired off-axis signals by applying proper delays and weights on the received signals to an array transducer. Minimum variance adaptive beamforming method achieved a great success in improving ultrasound imaging resolution due to the use of environmental information and updating the apodization for each point. In spite of its high performance, its computational complexity is as high as O(M-3) where M is the number of array elements. This paper describes a new approach to reduce computational complexity of the MV by applying the beamforming method in a cascade structure. At the first stage, the interference signals received from the points far from the main axis are strongly suppressed. At the second stage, the interference signals from points close to the focal point that are received by the array are eliminated.