A linearly constrained minimum variance beamformer with a pre-specified suppression level over a pre-defined broad null sector

摘要

We present a minimum variance beamformer with a pre-specified suppression level over a pre-defined angular null sector, which for example may be used when the interference moves across an a-priori known angular sector. To reduce the design computational complexity, we replace the original quadratic constraint of the optimization problem with a set of linear constraints. Given the suppression level, we determine the minimal number of linear constraints that ensures that the original quadratic constraint holds. Simulations show that the array gain of the proposed beamformer exceeds that of the classical minimum variance beamformer for a finite number of samples and coherent interference scenario, using the sample matrix inverse technique or the diagonal loading approach.