Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generalized principal component beamformer for communication systems
Signal Processing - Content-based image and video retrieval
Fuzzy inference based robust beamforming
Signal Processing
Information Sciences: an International Journal
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
Robust Capon beamformer under norm constraint
Signal Processing
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
Two-stage DOA estimation for CDMA multipath signals
Information Sciences: an International Journal
On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing
Adaptive step-size algorithms for blind interference suppression inDS/CDMA systems
IEEE Transactions on Signal Processing
An adaptive array robust to beam pointing error
IEEE Transactions on Signal Processing
Frequency estimation in multipath Rayleigh-sparse-fading channels
IEEE Transactions on Wireless Communications
Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review
Information Sciences: an International Journal
Fuzzy coefficient volatility (FCV) models with applications
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications
Applied Soft Computing
Expert Systems with Applications: An International Journal
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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.