Abstract
Bayesian beamformer is a modified version of the Constrained Least Mean Square (CLMS) beamformer to avoid performance degradation due to errors in the detection of the signal direction. It depends on repeating the process of the CLMS around the detected direction with the hope to meet the desired direction in one of the repetitions. All repetitions have repeated common part because this common part is implicit with the other part in the mathematical structure of the CLMS. Partial adaptation algorithms like Householder transform separate the implicit parts of the CLMS. The proposed algorithm exploits the partial adaptation to avoid common part repetition hence reducing the complexity. In the simulation stage, it has been found that the proposed algorithm overcomes another problem in the Bayesian beamformer which was not planned to be solved.
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