An adaptive robust fuzzy beamformer for steering vector mismatch and reducing interference and noise
This paper presents an efficient beamformer design that accounts for both signal steering vector mismatches and the trade-off between interference and noise reduction. The empirical results show that substantial performance degradation occurs when the exact signal steering vector for the desired signal is not known. Furthermore, total rejection of interference may increase noise, and vice versa. Therefore, an adaptive robust fuzzy beamformer was designed by adopting the method of fuzzy systems to modify both the value of the signal steering vector and the interference-to-noise ratio. This method uses the first-order Taylor expansion of the object function to modify the mismatches of the signal steering vector, and uses the signal covariance matrix eigendecomposition to adjust the ratio of interference reduction to noise reduction. Simulations confirm that the proposed scheme performance is substantially improved and more robust if the effects of the signal steering vector mismatches and the interference to noise ratio are considered in the beamformer design which is based on expert knowledge.
beamformer fuzzy system genetic algorithm antenna array minimum variance distortionless response